Artif Intell Rev (2019) 52:1495–1545
https://doi.org/10.1007/s10462-017-9599-6
A survey on classification techniques for opinion mining
and sentiment analysis
Fatemeh Hemmatian1 · Mohammad Karim Sohrabi1
Published online: 18 December 2017
© Springer Science+Business Media B.V., part of Springer Nature 2017
Abstract Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts
and perceptions from unstructured texts, which with regard to the emergence of online social
media and mass volume of users’ comments, has become to a useful, attractive and also
challenging issue. There are varieties of researches with different trends and approaches in
this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion
mining and sentiment analysis to classify available methods and compare their advantages
and drawbacks, in order to have better understanding of available challenges and solutions
to clarify the future direction. For this purpose, we present a proper framework of opinion
mining accompanying with its steps and levels and then we completely monitor, classify,
summarize and compare proposed techniques for aspect extraction, opinion classification,
summary production and evaluation, based on the major validated scientific works. In order
to have a better comparison, we also propose some factors in each category, which help to
have a better understanding of advantages and disadvantages of different methods.
Keywords Opinion mining · Sentiment analysis · Machine learning · Classification ·
Lexicon
1 Introduction
Due to the increasing development of web technology, different evaluation areas are growing
in this field. The original web had the static pages and the users didn’t allow manipulating
B Mohammad Karim Sohrabi
[email protected]
Fatemeh Hemmatian
[email protected]
1 Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran
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its contents. Nevertheless, with the advent of new programming technologies, the possibility
of interactions and getting feedback on the web pages grew increasingly. The major part of
these interactions includes the users’ comments, which lead to feedback for the owners of
the web pages to benefit from the users’ ideas to improve the future performances and causes
the products and services adapt with their target group in an appropriate manner. However,
manual analysis of such opinions, especially in the social networks with a lot of audience
through the world, is very difficult, time consuming and in some cases impossible.
To overcome these limitations, the opinion mining has been introduced as an effective
way to discover the knowledge through the expressed comments, especially in the context
of the web. Opinion mining or sentiment analysis extracts the users’ opinions, sentiments
and demands from the subjective texts in a specific domain and distinguishes their polarity.
The exponential and progressive increase of internet usage and the exchange of the public
thoughts are the main motivations of researches in opinion mining and sentiment analysis.
Since several data processing approaches (Sohrabi and Azgomi 2017a, b; Sohrabi and Ghods
2015), supervised and unsupervised machine learning techniques (Sohrabi and Akbari 2016),
data mining and knowledge discovery methods, including association rule mining (Sohrabi
and Marzooni 2016), frequent itemset mining (Sohrabi and Barforoush 2012, 2013; Sohrabi
and Ghods 2014; Sohrabi 2018), and sequential pattern mining (Sohrabi and Ghods 2016;
Sohrabi and Roshani 2017), with various applications (Arab and Sohrabi 2017; Sohrabi and
Tajik 2017; Sohrabi and Karimi 2018), and web mining approaches (Zhang et al. 2004;
Sisodia and Verma 2012), including web structure mining (WSM) (Velásquez 2013), web
usage mining (WUM) (Yin and Guo 2013), and web content mining (WCN) (Mele 2013),
have been represented in the literature, there are different choices to select techniques and
provide methods for opinion mining and sentiment analysis.
The research about the opinion mining began from the early 2000, but the phrase “opinion
mining” was firstly used in Dave et al. (2003) (Liu 2012). In the past 15 years, various
researches have been conducted to examine and analyze the opinions within news, articles,
and product and service reviews (Subrahmanian and Reforgiato 2008). Nowadays, most
people benefit from the opinions of different people by a simple search on the Internet
when buying a commodity or selecting a service. According to the study conducted in Li
and Liu (2014), 81% of the Internet users have searched related comments before buying
a commodity at least once. The search rates in related comments before using restaurants,
hotels and a variety of other services have been reported from 73 to 87%. It should be
noted that these online investigations had a significant impact on the customer’s decisions.
People’s sentimental ideas and theories can be extracted from different web resources, such
as blogs (Alfaro et al. 2016; Bilal et al. 2016), review sites (Chinsha and Joseph 2015;
Molina-González et al. 2014; Jeyapriya and Selvi 2015), and recently micro-blogs (Balahur
and Perea-Ortega 2015; Feng et al. 2015; Pandarachalil et al. 2015; Da Silva et al. 2016;
Saif et al. 2016; Wu et al. 2016; Ma et al. 2017; Li et al. 2017; Keshavarz and Abadeh 2017;
Huang et al. 2017). Micro-blogs, such as Twitter, have become very popular among users
and provides the possibility of sending tweets up to a specified limited number of characters
(Liu 2015).
Opinion mining, can take place in three levels of the document (Sharma et al. 2014;
Moraes et al. 2013; Tang et al. 2015; Sun et al. 2015; Xia et al. 2016), sentence (Marcheggiani
et al. 2014; Yang and Cardie 2014) and aspect (Chinsha and Joseph 2015; Marrese-Taylor
et al. 2014; Wang et al. 2017b). Also all techniques which used to sentiment analysis can be
categorized into three main classes as: machine learning techniques (Pang et al. 2002; Moraes
et al. 2013; Saleh et al. 2011; Habernal et al. 2015; Riaz et al. 2017; Wang et al. 2017a),
lexicon-based approaches (Kanayama and Nasukawa 2006; Dang et al. 2010; Pandarachalil
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Data Mining Web Mining
Web Structure
Mining
Web Usage
Mining
Web Content Mining
Opinion Mining
Fig. 1 The position of opinion mining
et al. 2015; Saif et al. 2016; Taboada et al. 2011; Turney 2002; Molina-González et al. 2015;
Qiu et al. 2011; Liao et al. 2016; Bravo-Marquez et al. 2016; Muhammad et al. 2016; Khan
et al. 2017) and hybrid methods (Balahur et al. 2012; Abdul-Mageed et al. 2014; Keshavarz
and Abadeh 2017). The machine learning-based opinion mining techniques which have the
benefit of using well-known machine learning algorithms, can be divided into three groups:
supervised (Jeyapriya and Selvi 2015; Habernal et al. 2015; Severyn et al. 2016; Anjaria and
Guddeti 2014), semi-supervised (Hajmohammadi et al. 2015; Hong et al. 2014; Gao et al.
2014; Carter and Inkpen 2015; Lu 2015) and unsupervised (Li and Liu 2014; Claypo and
Jaiyen 2015; De and Kopparapu 2013) methods. Lexicon-based method relies on a dictionary
of sentiments and has been highly regarded in the recent studies which can be divided into
the dictionary-based method (Chinsha and Joseph 2015; Pandarachalil et al. 2015; Saif et al.
2016; Sharma et al. 2014) and corpus-based method (Turney 2002; Molina-González et al.
2015; Keshtkar and Inkpen 2013; Vuli´c et al. 2015). There are also very few works that
are used both corpus-based and dictionary-based methods to improve the results (Taboada
et al. 2011). Some literature reviews and books on opinion mining and sentiment analysis
techniques and methods have also been represented before, which have investigated the
problem from different points of views (Bouadjenek et al. 2016; Liu 2015).
The rest of paper is organized as follows: The clear explanation of the problem, process,
tasks and applications of opinion mining has been represented in Sect. 2. Section 3 defines
the levels of opinion mining. Section 4 focuses on extraction of aspects. The classification
and comparison of sentiments analysis techniques are presented in Sect. 5. The evaluation
criteria in the opinion mining are discussed in Sect. 6, the future direction of opinion mining
are represented in Sect. 7, and finally Sect. 8 concludes the review.
2 Opinion mining: process, tasks, and applications
Opinion mining can be considered as a new subfield of natural language processing (Daud
et al. 2017), information retrieval (Scholer et al. 2016), and text mining (Singh and Gupta
2017). Figure 1 represents the position of opinion mining. Opinion mining is actually considered as a subset of the web content mining process in the web mining research area. Since the
web content mining focuses on the contents of the web and texts have formed large volume
of web content, text mining techniques are widely used in this area. The most important
challenge of using text mining in web content is their unstructured or semi-structured nature
that requires the natural language processing techniques to deal with. Web mining itself is
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also considered a subset of the data mining research area. Here, the use of data mining is to
discover the knowledge from massive data sources of the web.
2.1 Opinion mining definitions
The main goal of opinion mining is to automate extraction of sentiments expressed by users
from unstructured texts. Two major definitions of opinion mining can be seen in the literature.
The first definition is proposed in Saleh et al. (2011), as “The automatic processing of documents to detect opinion expressed therein, as a unitary body of research”. The second major
definition says: “Opinion mining is extracting people’s opinion from the web. It analyzes
people’s opinions, appraisals, attitudes, and emotions toward organizations, entities, person,
issues, actions, topic and their attribute” (Jeyapriya and Selvi 2015; Liu 2012; Liu and Zhang
2012).
Opinion mining contains several tasks with different names which all of them are covered
by it (Liu 2012):
• Sentiment Analysis The purpose of sentiment analysis is the sentiment recognition and
public opinion examination that is considered as a research area in the field of text mining.
• Opinion extraction The process of extraction of users’ opinions from the web documents
is called opinion extraction. The main purpose of opinion extraction is to find out the
users’ ways of thinking.
• Sentiment mining Sentiment mining has two main goals: first, it determines whether
the given text contains objective or subjective sentences. A sentence is called objective
(or factual), when it contains the factual information about the product. The subjective
sentences represent the individual emotions about the desired product. In the opinion
mining we consider the subjective sentences. Second, it extracts opinions and classifies
them into three categories of positive, negative and neutral (Farra et al. 2010).
• Subjection analysis Subjection analysis provides the possibility to identify, classify, and
collect subjective sentences.
• Affect or emotion analysis Many of the words at the text are emotionally positive or
negative. Affect analysis specifies the aspects that are expressing emotions in the text
using the natural language processing techniques (Grefenstette et al. 2004).
• Review mining Review mining is a sub-topic of text sentiment analysis and its main
purpose is to extract aspects from the authors’ sentiments and is to produce a summary of
the sentiments. More researches in the review mining have been focused on the product
reviews (Zhuang et al. 2006).
2.2 Opinion mining procedure
The main objective of the opinion mining is to discover all sentiments exist in the documents
(Saleh et al. 2011); in fact, it determines the speaker’s or writer’s attitude about the different
aspects of a problem. We have modeled the opinion mining process in Fig. 2, in which, each
part has some obligations which are as follows:
1. Data collection Having a comprehensive and reliable dataset is the first step to perform
opinion mining process. The necessary information could be collected from various web
resources, such as weblogs, micro blogs (such as Twitter1), social networks (such as
1 https://www.twitter.com/.
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Aspects
Opinion active
words or phrases
Datasets Data Collection Opinion
Identification
Aspect
Extraction
Opinion
Classification
Positive
Negative
Production
Summary Evaluation
Fig. 2 Opinion mining process
Facebook2) and review websites (such as Amazon,3 Yelp,4 and Tripadvisor5). Using
tools that are developed for extracting data through web, and using various techniques
such as web scraping (Pandarachalil et al. 2015), can be useful to collect appropriate
data. Some datasets are provided in English which can be used as references (Pang et al.
2002; Pang and Lee 2004; Blitzer et al. 2007). Researchers can apply their methods
on these datasets for their simplicity. The first dataset6 prepared by Pang et al. (2002)
includes 1000 positive movie reviews and 1000 negative movie reviews. This dataset
is the most important and the oldest dataset in this area. The second dataset7 prepared
by Pang and Lee (2004), which includes 1250 positive reviews, 1250 negative reviews,
and 1250 neutral reviews. The Third one is Blitzer (Blitzer et al. 2007),8 which includes
1000 positive movie review and 1000 negative movie reviews. Table 1 shows the obtained
accuracies of different researches on the benchmark datasets.
2. Opinion identification All the comments should be separated and identified from the
presented texts in this phase. Then the extracted comments should be processed to separate
the inappropriate and fake ones. What we mean by opinions is all the phrases representing
the individual emotions about the products, services or any other desired category.
3. Aspect extraction In this phase, all the existing aspects are identified and extracted
according to the procedures. Selecting the potential aspects could be very effective in
improving the classification.
4. Opinion classification After opinion identification and aspect extraction which can be
considered as the preprocessing phase, in this step the opinions are classified using
different techniques which this paper summarizes, classifies and compares them.
5. Production summary Based on the results of the previous steps, in the production
summary level, a summary of the opinion results is produced which can be in different
forms such as text, charts etc.
6. Evaluation the performance of opinion classification can be evaluated using four evaluation parameters, namely accuracy, precision, recall and f-score.
2 https://www.facebook.com/.
3 http://www.amazon.com/.
4 http://www.yelp.com/.
5 http://www.tripadvisor.com/.
6 http://www.cs.cornell.edu/people/pabo/movie-review-data/.
7 http://www.cs.cornell.edu/people/pabo/movie-review-data/ (review corpus version 2.0).
8 http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.
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Table 1 Obtained accuracies on the benchmark datasets
Datasets Papers Accuracy (%)
Pang et al. (2002) Chen et al. (2011) 64
Li and Liu (2012) 77
Pang and Lee (2004) Penalver-Martinez et al. (2014) 89.6
Fernández-Gavilanes et al. (2016) 69.95
Fersini et al. (2016) 81.7
Saleh et al. (2011) 85.35
Boiy and Moens (2009) 87.40
Blitzer et al. (2007) Xia et al. (2016) 80
Xia et al. (2011) 85.58
Poria et al. (2014) 87
2.3 Opinion mining applications
Sentiment analysis tries to describe and assess the expressed sentiments about the issues
of interest to web users which have been mentioned in textual messages. These issues can
include a range of brands or goods up to the broader favorite topics such as social, political,
economic and cultural affairs. We note to the several major applications of the opinion mining
in this section.
2.3.1 Opinion mining in the commercial product areas
The usage of opinion mining in the area of commercial products (Chen et al. 2014; MarreseTaylor et al. 2014; Jeyapriya and Selvi 2015; Li et al. 2012; Luo et al. 2015) is important
from three viewpoints:
1. The individual customers’ point of view: when someone wants to buy a product, having
a summary of the others’ opinions can be more useful than studying the massive amounts
of others’ comments about this product. Moreover, the customer will be able to compare
the products easily by having a summary of the opinions.
2. The business organizations and producers’ point of view: this issue is important for
the organizations to improve their products. This information is used not only for the
product marketing and evaluation but also for product design and development. The
manufacturing companies can even increase, decrease or change the products based on
customer’s opinions.
3. The advertising companies’ point of view: the opinions are important for advertising
companies because they can obtain ideas of the market demand. The public perspective
of the people and type of products that they are interested in can be found among the
items that extracted by opinion mining.
The important achievements of opinion mining in the commercial products are as follows
(Tang et al. 2009):
• Products comparison Online sellers want their customers to comment about the purchased products. Due to the increasing use of the online marketing and such web services,
these sentiments are growing. These sentiments are useful both for product manufacturers and consumers because they can have a better decision making by comparing the
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sentiments and ideas of others on this product. More researches have been carried out in
this area, which have focused on the issue of automatic classification of the products in
two categories of recommended and non-recommended.
• Sentiments summarization When the number of sentiments increase, its recognition
is difficult either for producers or consumers. With the sentiments summarization, customers find out easier the sentiments of other customers about the product and also
manufacturers realize easier to the customers’ sentiments about the products as well.
• Exploring the reason of opinion The reason of the user to give an opinion can also be
extracted in the opinion mining process. It is extremely important to determine the reason
why consumers like or dislike the product.
2.3.2 Opinion mining in the politics area
Along with the comments on the sale and purchase of goods, with the widespread and
comprehensive use of the Internet services by people, users can also comment on various
political, social, religious, and cultural issues. Collecting and analyzing these comments helps
greatly to politicians, managers of social issues or religious and cultural activists to take
appropriate decisions for improving the social life of the community. One of the significant
applications among these areas is in the political elections that individuals can benefit from
the sentiments of others to make decision in their voting. Analyzing opinions existed in social
networks related to election is addressed in (Tsakalidis et al. 2015; Unankard et al. 2014;
Kagan et al. 2015; Mohammad et al. 2015; Archambault et al. 2013).
2.3.3 Opinion mining in the stock market and stock forecast
Achieving sustained and long-term economic growth requires optimal allocation of resources
at the national economy level and this is not easily possible without the help of appropriate
information and knowledge. Investing in supplied stocks in the stock exchange is one of
the profitable options in the capital market which plays an important role in the individuals’
better decision making and having its own particular audience which predicting the stock.
Among the studies representing the application of the opinion mining in the stock market it
can be pointed out to (Bollen et al. 2011; Nofer and Hinz 2015; Bing et al. 2014; Fortuny
et al. 2014) that the opinions have been used to predict the stock market. For example, Daily
comments of Twitter have been analyzed using OpinionFinder and GPOMS as two important
moods tracking tools by Bollen et al. (2011) and showed the correlation to daily changes in
Dow Jones Industrial Average closing values.
3 Levels of opinion mining
As shown in Fig. 3, opinion mining is possible on four different levels, namely document
level, sentence level, aspect level, and concept level.
Document level (Moraes et al. 2013) of opinion mining is the most abstract level of
sentiment analysis and so is not appropriate for precise evaluations. The result of this level of
analysis is usually general information about the documents polarity which cannot be very
accurate. Sentence level opinion mining (Marcheggiani et al. 2014) is a fine-grain analysis
that could be more accurate. Since the polarity of the sentences of an opinion does not imply
the same polarity for the whole of opinion necessarily, aspect level of opinion mining (Xia
et al. 2015) have been considered by researchers as the third level of opinion mining and
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Opinion Mining Levels
Document Level Sentence Level Aspect Level Concept Level
Fig. 3 Different levels of opinion mining
sentiment analysis. Concept level opinion mining is the forth level of sentiment analysis
which focuses on the semantic analysis of the text and analyzes the concepts which do not
explicitly express any emotion (Poria et al. 2014). Several recent surveys and reviews on
sentiment analysis consider these levels of opinion mining from this point of view (Medhat
et al. 2014; Ravi and Ravi 2015; Balazs and Velasquez 2016; Yan et al. 2017; Sun et al. 2017;
Lo et al. 2017).
3.1 Document level
The sentiment analysis may be used in the document level. In this level of the opinion
mining, sentiments are ultimately summarized on the whole of the document as positive or
negative (Pang et al. 2002). The purpose of categorizing comments at the document level is
the automatic classification of information based on a single topic, which is expressed as a
positive or negative sentiment (Moraes et al. 2013). Since this level of opinion mining does
not enter into details and the review process takes place in an abstract and general view, the
mining process can be done much faster. In early works, most of the researches conducted
at the document level and focused on datasets such as the news and the products review.
By increase in the popularity of the social networks, different types of datasets were created
which made increasing the studies of this level (Habernal et al. 2015; Gupta et al. 2015).
Since the entire document is considered as a single entity in document level opinion mining,
this level of opinion mining is not suitable for precise evaluation and comparison. Most of
the techniques carrying out in the opinion classification at this level are based on supervised
learning methods (Liu and Zhang 2012).
3.2 Sentence level
Since, document level sentiment analysis is too coarse, researchers investigated approaches
to focus on the sentence (Wilson et al. 2005; Marcheggiani et al. 2014; Yang and Cardie
2014; Appel et al. 2016). The goal of this level of opinion mining is to classify opinions in
each sentence. Sentiments analysis on the sentence level constitutes of two following steps
(Liu and Zhang 2012):
• Firstly it is determined that the sentence is subjective or objective.
• Secondly the polarity (positive or negative) of sentence is determined.
In the classification of comments at the sentence level, since the documents are broken into
several sentences, they provide more accurate information on the polarity of the views and
naturally entail more challenges than the level of the document.
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3.3 Aspect level
Although the classification of text sentiments on the document and sentence level is helpful
in many cases but it does not provide all the necessary details. For example, being positive
of the sentiments on a document in relation to a particular entity, does not imply that the
author’s opinion is positive about all the aspects of an entity. Similarly, negative sentiments
do not represent the author negative opinion about all the aspects of an entity (Liu and
Zhang 2012). The classification on the document level (Moraes et al. 2013) and sentence
level (Marcheggiani et al. 2014) does not provide these kinds of information and we need to
perform opinion mining in aspect level (Xia et al. 2015) to achieve these details. When the
considered comment does not include a single entity or aspect, this level of opinion mining
is the appropriate option, which is an important advantage of this level of classification and
distinguishes it from the two previous levels. Aspect level opinion mining actually considers
the given opinion itself instead of looking to the language structures (document, sentence or
phrase) (Liu 2012). The objective of this level is to identify and extract the aspects from the
sentiments text and then specify their polarity. This level of sentiments analysis can produce
a summary of the sentiments about different aspects of the desired entity. It can be seen that
this level of opinion mining provides a more accurate result (Chinsha and Joseph 2015).
3.4 Concept level
Cambria (2013) introduced the concept level opinion mining as a deep understanding of the
natural language texts by the machine, in which, the opinion methods should go beyond the
surface level analysis. Cambria et al. (2013) has also presented the concept level of opinion
mining as a new avenue in the sentiment analysis. The analysis of emotions at the concept level
is based on the inference of conceptual information about emotion and sentiment associated
with natural language. Conceptual approaches focus on the semantic analysis of the text
and analyze the concepts which do not explicitly express any emotion (Poria et al. 2014).
An enhanced version of SenteicNet have been proposed in Poria et al. (2013), which assign
emotion labels to carry out concept level opinion mining. Poria et al. (2014) have proposed a
new approach to improve the accuracy of polarity detection. An analysis of comments at the
conceptual level has been introduced that integrates linguistic, common-sense computing, and
machine learning techniques. Their results indicate that the proposed method has a desirable
accuracy and better than common statistical methods. A concept level sentiment dictionary
has been built in Tsai et al. (2013) based on common-sense knowledge using a two phase
method which integrates iterative regression and random walk with in-link normalization. A
concept level sentiment analysis system has been presented in Mudinas et al. (2012), which
combined lexicon-based and learning based approaches for concept mining from opinions.
EventSensor system is represented in Shah et al. (2016) to extract concept tags from visual
contents and textual meta data in concept-level sentiment analysis.
4 Aspect extraction
One of the main important steps in sentiments classification is aspect extraction (Rana and
Cheah 2016). In this section we categorize current techniques for aspect extraction and
selection. As it mentioned, aspect level classification has better performance and a prerequisite
for using it is obtaining aspects. Most researches in the field of aspect extraction have been
focused on the online reviews (Hu and Liu 2004; Li et al. 2015; Lv et al. 2017). In general,
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Aspect Extraction Techniques
Based on exploiting opinion
and aspect relations
Based on nouns and the
frequent noun phrases
Based on topic
modeling
Based on the supervised
learning techniques
Fig. 4 Classification of aspect extraction techniques
as is shown in Fig. 4, the related techniques can be placed in four categories (Liu 2012):
Extraction based on the frequent noun phrases and nouns (Jeyapriya and Selvi 2015; Hu and
Liu 2004; Li et al. 2015), Extraction based on exploiting opinion and aspect relations (Qiu
et al. 2011; Wu et al. 2009), Extraction based on the supervised learning (Jin et al. 2009;
Yu et al. 2011), Extraction based on topic modeling (Vuli´c et al. 2015; Mukherjee and Liu
2012).
4.1 Extraction based on frequency of noun phrases and nouns
This method is known as a simple and effective approach. Generally, when people express
their comments about various aspects of a product, they basically use similar words frequently
to express their sentiments (Liu 2012). In this method, the nouns and noun phrases are
determined by a POS tagger and the names that have been frequently repeated are selected
as aspect. POS tags indicate the role of the words in a sentence (Wang et al. 2015a). A list of
POS tags has been collected in Table 2 which shows all the POS tags based on (Liu 2012).
Li et al. (2015) suggested a method for improving feature extraction performance by
online reviews. Their method which is based on frequent noun and noun phrase is consisted
of three important components: frequent based mining and pruning, order based filtering,
and similarity based filtering. Their experimental results show that proposed method could
be generalized over various domains with different-sized data. Jeyapriya and Selvi (2015)
suggested a feature extraction system in product review. They extracted nouns and noun
phrase from each review sentence and used minimum support threshold to find frequent
features in review sentences. Their accuracy was about 80.32
4.2 Extraction based on relation exploitation between opinion words and aspects
This method uses the existing relationship between the aspects and opinion words in the
expressed opinions. Some of the infrequent aspects can be identified with the help of this
method. The main idea is that the opinion words can be used to describe the different
aspects (Liu and Zhang 2012). Qiu et al. (2011) focused on two fundamental and important
issues, opinion lexicon expansion and target extraction, and suggested the double propagation
approach. They performed extraction based on syntactic relations that cause link between
review words and targets. Relations could be detected according to a dependency parser and
then could be used for opinion lexicon expansion and extraction. Results show that their
approach is significantly better than existing methods.
4.3 Extraction based on the supervised learning
Supervised learning approaches are promising techniques for aspect extraction which generates a model of aspects by using labeled data. Support vector machine (SVM) (Cortes and
Vapnik 1995; Manek et al. 2017), conditional random fields (CRF) (Lafferty et al. 2001), and
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A survey on classification techniques for opinion mining… 1505 Table 2 POS tags (Liu 2012) Description Tag Description Tag Description Tag Adjective JJ Comparative adjectives JJR Superlative adjectives JJS Adverb RB Comparative adverb RBR Superlative adverb RBS Noun, plural noun, singular NNS noun, singular or mass NN Comparative adjective JJR Coordinating conjunction CC Subordination conjunction IN Interjection UH Determiner DT Verb, gerund or present participle VBG List item marker LS Model verb MV Cardinal number CD Adjective JJ Verb, past participle VBN Verb, gerund or present participle VBG Particle RP Verb, past tense VBD Personal pronoun PP Possessive ending POS Verb, non-3rd-person singular/p VBP Verb, 3rd-person singular present VBZ Proper noun, plural NPS Proper noun, singular NP Symbol SYM Verb, base form VB
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hidden Markov model (HMM) (Rabiner 1989) are some of the supervised learning methods
that can be used to extract the aspects (Liu 2012). In Kobayashi et al. (2007), aspects was
extracted from a collection of blog posts using machine learning methods and the results was
used as statistical patterns for aspect extraction.
4.3.1 Extraction based on topic modeling
In recent years the statistical topic models are considered as a systematic approach to detect
the topics from the text document collections (Vuli´c et al. 2015; Mukherjee and Liu 2012; Liu
2012). Topic modeling is an unsupervised method for aspect extraction in which it assumes
that any document contains k hidden topics. For example in hotel investigation, some standard
features such as location, cleanliness and so on are discussed. Now it is possible that there are
comments about the quality of internet connection, so we are facing with a hidden topic. In
these situations, there is a need for a model to automatically extract relevant aspects without
human supervision (Titov and McDonald 2008). Since this approach, uses statistical methods
like latent semantic analysis (LSA) (Hofmann 1999) and latent Dirichlet allocation (LDA)
(Blei et al. 2003), it is called statistical models too. Also LDA and LSA use the bag of words
represented in documents, so they can be used only in document level opinion mining. Ma
et al. (2015) proposed an approach of probabilistic topic model based on LDA in order to
semantic search over citizens opinions about city issues on online platforms. Their results
show that systems based on LDA provide useful information about their staff members. Luo
et al. (2015) worked on detection and rating feature in product review which is as important
task of opinion mining in aspect level. They presented Quad-tuple PLSA for solving this
problem because entity and rating rarely considered in previous researches hence had great
performance.
5 Opinion classification techniques
Most important and critical step of opinion mining is selecting an appropriate technique to
classify the sentiments. In this section we explain, categorize, summarize and compare proposed techniques in this area. The classification methods which are proposed in the literature
can be fall into two groups: machine learning and lexicon-based approaches. This type of
categorization can be seen in some works (Wang et al. 2015a; Petz et al. 2015), but in this
paper we address the issue much more comprehensive in more details with better comparison
factors and discussions based on the major validated scientific works.
5.1 Machine learning method
According to Fig. 5 three techniques of the machine learning methods are used to classify
the sentiments: supervised, semi-supervised, and unsupervised learning methods.
5.1.1 Supervised learning method
In the supervised learning is, the process of learning is carried out using the data of a training
set in which, the output value is specified for any input and the system tries to learn a function,
by mapping the input to the output, i.e., to guess the relationship between input and output. In
this method, the categories are initially specified and any of the training data is assigned to a
specific category. In fact in the supervised approach, the classifier categorizes the sentiments
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Machine Learning Methods
Unsupervised
Learning
Semi-supervised
Learning
Supervised
Learning
Fig. 5 Machine learning based opinion classification techniques
Supervised Learning
Probabilistic
Classification
Non-probabilistic
Classification
Naïve
Bayes
Bayesian
Network
Maximum
Entropy
Support Vector
Machine
Neural
Network
K-Nearest
Neighbour
Decision
Tree
Rulebased
Fig. 6 Supervised learning-based opinion classification methods
using labeled text samples. As shown in Fig. 6, supervised sentiment classification approaches
can be divided into two main categories: Probabilistic Classification and Non-probabilistic
Classification.
5.1.1.1. Probabilistic classification Probabilistic classification is one of the popular classifications approaches in the field of the machine learning. These methods are derived from
probabilistic models which provide a systematic way for statistical classification in complex
domains such as the natural language. Hence, it has an effective application in the opinion
mining. Naive Bayes, Bayesian network and maximum entropy are some of the well-known
methods in the field of opinion mining which belong to this kind of classification.
Naive Bayes (NB)
This method is a simple and popular approach in the area of text classification. It is assumed
that the existing sentences within the document are subjective which the existence of semantic
orientation of words is definitely a final verdict on the subjectivity of the sentences. The
features are also selected from a set of words within the documents. It is an approach to
text classification that assigns the class c∗ = Arg maxc P (c|d), to a given document d. The
relation 1 is expressed based on the Bayesian theory (Pang et al. 2002).
P(c|d) = p(c)p(d|c)
p(d) (1)
where p(d) plays no role in selecting c∗. To estimate the term p(d|c), Naïve Bayes decomposes it by assuming the fi’s are conditionally independent given d’s class as in relation 2
(Pang et al. 2002).
PN B(c|d) = P(C)
πm
i=1P ( fi|c)
ni(d)
p(d) (2)
where m is the number of features and f is the feature vector.
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Artif Intell Rev (2019) 52:1495–1545
https://doi.org/10.1007/s10462-017-9599-6
A survey on classification techniques for opinion mining
and sentiment analysis
Fatemeh Hemmatian1 · Mohammad Karim Sohrabi1
Published online: 18 December 2017
© Springer Science+Business Media B.V., part of Springer Nature 2017
Abstract Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts
and perceptions from unstructured texts, which with regard to the emergence of online social
media and mass volume of users’ comments, has become to a useful, attractive and also
challenging issue. There are varieties of researches with different trends and approaches in
this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion
mining and sentiment analysis to classify available methods and compare their advantages
and drawbacks, in order to have better understanding of available challenges and solutions
to clarify the future direction. For this purpose, we present a proper framework of opinion
mining accompanying with its steps and levels and then we completely monitor, classify,
summarize and compare proposed techniques for aspect extraction, opinion classification,
summary production and evaluation, based on the major validated scientific works. In order
to have a better comparison, we also propose some factors in each category, which help to
have a better understanding of advantages and disadvantages of different methods.
Keywords Opinion mining · Sentiment analysis · Machine learning · Classification ·
Lexicon
1 Introduction
Due to the increasing development of web technology, different evaluation areas are growing
in this field. The original web had the static pages and the users didn’t allow manipulating
B Mohammad Karim Sohrabi
[email protected]
Fatemeh Hemmatian
[email protected]
1 Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran
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1496 F. Hemmatian, M. K. Sohrabi
its contents. Nevertheless, with the advent of new programming technologies, the possibility
of interactions and getting feedback on the web pages grew increasingly. The major part of
these interactions includes the users’ comments, which lead to feedback for the owners of
the web pages to benefit from the users’ ideas to improve the future performances and causes
the products and services adapt with their target group in an appropriate manner. However,
manual analysis of such opinions, especially in the social networks with a lot of audience
through the world, is very difficult, time consuming and in some cases impossible.
To overcome these limitations, the opinion mining has been introduced as an effective
way to discover the knowledge through the expressed comments, especially in the context
of the web. Opinion mining or sentiment analysis extracts the users’ opinions, sentiments
and demands from the subjective texts in a specific domain and distinguishes their polarity.
The exponential and progressive increase of internet usage and the exchange of the public
thoughts are the main motivations of researches in opinion mining and sentiment analysis.
Since several data processing approaches (Sohrabi and Azgomi 2017a, b; Sohrabi and Ghods
2015), supervised and unsupervised machine learning techniques (Sohrabi and Akbari 2016),
data mining and knowledge discovery methods, including association rule mining (Sohrabi
and Marzooni 2016), frequent itemset mining (Sohrabi and Barforoush 2012, 2013; Sohrabi
and Ghods 2014; Sohrabi 2018), and sequential pattern mining (Sohrabi and Ghods 2016;
Sohrabi and Roshani 2017), with various applications (Arab and Sohrabi 2017; Sohrabi and
Tajik 2017; Sohrabi and Karimi 2018), and web mining approaches (Zhang et al. 2004;
Sisodia and Verma 2012), including web structure mining (WSM) (Velásquez 2013), web
usage mining (WUM) (Yin and Guo 2013), and web content mining (WCN) (Mele 2013),
have been represented in the literature, there are different choices to select techniques and
provide methods for opinion mining and sentiment analysis.
The research about the opinion mining began from the early 2000, but the phrase “opinion
mining” was firstly used in Dave et al. (2003) (Liu 2012). In the past 15 years, various
researches have been conducted to examine and analyze the opinions within news, articles,
and product and service reviews (Subrahmanian and Reforgiato 2008). Nowadays, most
people benefit from the opinions of different people by a simple search on the Internet
when buying a commodity or selecting a service. According to the study conducted in Li
and Liu (2014), 81% of the Internet users have searched related comments before buying
a commodity at least once. The search rates in related comments before using restaurants,
hotels and a variety of other services have been reported from 73 to 87%. It should be
noted that these online investigations had a significant impact on the customer’s decisions.
People’s sentimental ideas and theories can be extracted from different web resources, such
as blogs (Alfaro et al. 2016; Bilal et al. 2016), review sites (Chinsha and Joseph 2015;
Molina-González et al. 2014; Jeyapriya and Selvi 2015), and recently micro-blogs (Balahur
and Perea-Ortega 2015; Feng et al. 2015; Pandarachalil et al. 2015; Da Silva et al. 2016;
Saif et al. 2016; Wu et al. 2016; Ma et al. 2017; Li et al. 2017; Keshavarz and Abadeh 2017;
Huang et al. 2017). Micro-blogs, such as Twitter, have become very popular among users
and provides the possibility of sending tweets up to a specified limited number of characters
(Liu 2015).
Opinion mining, can take place in three levels of the document (Sharma et al. 2014;
Moraes et al. 2013; Tang et al. 2015; Sun et al. 2015; Xia et al. 2016), sentence (Marcheggiani
et al. 2014; Yang and Cardie 2014) and aspect (Chinsha and Joseph 2015; Marrese-Taylor
et al. 2014; Wang et al. 2017b). Also all techniques which used to sentiment analysis can be
categorized into three main classes as: machine learning techniques (Pang et al. 2002; Moraes
et al. 2013; Saleh et al. 2011; Habernal et al. 2015; Riaz et al. 2017; Wang et al. 2017a),
lexicon-based approaches (Kanayama and Nasukawa 2006; Dang et al. 2010; Pandarachalil
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Data Mining Web Mining
Web Structure
Mining
Web Usage
Mining
Web Content Mining
Opinion Mining
Fig. 1 The position of opinion mining
et al. 2015; Saif et al. 2016; Taboada et al. 2011; Turney 2002; Molina-González et al. 2015;
Qiu et al. 2011; Liao et al. 2016; Bravo-Marquez et al. 2016; Muhammad et al. 2016; Khan
et al. 2017) and hybrid methods (Balahur et al. 2012; Abdul-Mageed et al. 2014; Keshavarz
and Abadeh 2017). The machine learning-based opinion mining techniques which have the
benefit of using well-known machine learning algorithms, can be divided into three groups:
supervised (Jeyapriya and Selvi 2015; Habernal et al. 2015; Severyn et al. 2016; Anjaria and
Guddeti 2014), semi-supervised (Hajmohammadi et al. 2015; Hong et al. 2014; Gao et al.
2014; Carter and Inkpen 2015; Lu 2015) and unsupervised (Li and Liu 2014; Claypo and
Jaiyen 2015; De and Kopparapu 2013) methods. Lexicon-based method relies on a dictionary
of sentiments and has been highly regarded in the recent studies which can be divided into
the dictionary-based method (Chinsha and Joseph 2015; Pandarachalil et al. 2015; Saif et al.
2016; Sharma et al. 2014) and corpus-based method (Turney 2002; Molina-González et al.
2015; Keshtkar and Inkpen 2013; Vuli´c et al. 2015). There are also very few works that
are used both corpus-based and dictionary-based methods to improve the results (Taboada
et al. 2011). Some literature reviews and books on opinion mining and sentiment analysis
techniques and methods have also been represented before, which have investigated the
problem from different points of views (Bouadjenek et al. 2016; Liu 2015).
The rest of paper is organized as follows: The clear explanation of the problem, process,
tasks and applications of opinion mining has been represented in Sect. 2. Section 3 defines
the levels of opinion mining. Section 4 focuses on extraction of aspects. The classification
and comparison of sentiments analysis techniques are presented in Sect. 5. The evaluation
criteria in the opinion mining are discussed in Sect. 6, the future direction of opinion mining
are represented in Sect. 7, and finally Sect. 8 concludes the review.
2 Opinion mining: process, tasks, and applications
Opinion mining can be considered as a new subfield of natural language processing (Daud
et al. 2017), information retrieval (Scholer et al. 2016), and text mining (Singh and Gupta
2017). Figure 1 represents the position of opinion mining. Opinion mining is actually considered as a subset of the web content mining process in the web mining research area. Since the
web content mining focuses on the contents of the web and texts have formed large volume
of web content, text mining techniques are widely used in this area. The most important
challenge of using text mining in web content is their unstructured or semi-structured nature
that requires the natural language processing techniques to deal with. Web mining itself is
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1498 F. Hemmatian, M. K. Sohrabi
also considered a subset of the data mining research area. Here, the use of data mining is to
discover the knowledge from massive data sources of the web.
2.1 Opinion mining definitions
The main goal of opinion mining is to automate extraction of sentiments expressed by users
from unstructured texts. Two major definitions of opinion mining can be seen in the literature.
The first definition is proposed in Saleh et al. (2011), as “The automatic processing of documents to detect opinion expressed therein, as a unitary body of research”. The second major
definition says: “Opinion mining is extracting people’s opinion from the web. It analyzes
people’s opinions, appraisals, attitudes, and emotions toward organizations, entities, person,
issues, actions, topic and their attribute” (Jeyapriya and Selvi 2015; Liu 2012; Liu and Zhang
2012).
Opinion mining contains several tasks with different names which all of them are covered
by it (Liu 2012):
• Sentiment Analysis The purpose of sentiment analysis is the sentiment recognition and
public opinion examination that is considered as a research area in the field of text mining.
• Opinion extraction The process of extraction of users’ opinions from the web documents
is called opinion extraction. The main purpose of opinion extraction is to find out the
users’ ways of thinking.
• Sentiment mining Sentiment mining has two main goals: first, it determines whether
the given text contains objective or subjective sentences. A sentence is called objective
(or factual), when it contains the factual information about the product. The subjective
sentences represent the individual emotions about the desired product. In the opinion
mining we consider the subjective sentences. Second, it extracts opinions and classifies
them into three categories of positive, negative and neutral (Farra et al. 2010).
• Subjection analysis Subjection analysis provides the possibility to identify, classify, and
collect subjective sentences.
• Affect or emotion analysis Many of the words at the text are emotionally positive or
negative. Affect analysis specifies the aspects that are expressing emotions in the text
using the natural language processing techniques (Grefenstette et al. 2004).
• Review mining Review mining is a sub-topic of text sentiment analysis and its main
purpose is to extract aspects from the authors’ sentiments and is to produce a summary of
the sentiments. More researches in the review mining have been focused on the product
reviews (Zhuang et al. 2006).
2.2 Opinion mining procedure
The main objective of the opinion mining is to discover all sentiments exist in the documents
(Saleh et al. 2011); in fact, it determines the speaker’s or writer’s attitude about the different
aspects of a problem. We have modeled the opinion mining process in Fig. 2, in which, each
part has some obligations which are as follows:
1. Data collection Having a comprehensive and reliable dataset is the first step to perform
opinion mining process. The necessary information could be collected from various web
resources, such as weblogs, micro blogs (such as Twitter1), social networks (such as
1 https://www.twitter.com/.
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Aspects
Opinion active
words or phrases
Datasets Data Collection Opinion
Identification
Aspect
Extraction
Opinion
Classification
Positive
Negative
Production
Summary Evaluation
Fig. 2 Opinion mining process
Facebook2) and review websites (such as Amazon,3 Yelp,4 and Tripadvisor5). Using
tools that are developed for extracting data through web, and using various techniques
such as web scraping (Pandarachalil et al. 2015), can be useful to collect appropriate
data. Some datasets are provided in English which can be used as references (Pang et al.
2002; Pang and Lee 2004; Blitzer et al. 2007). Researchers can apply their methods
on these datasets for their simplicity. The first dataset6 prepared by Pang et al. (2002)
includes 1000 positive movie reviews and 1000 negative movie reviews. This dataset
is the most important and the oldest dataset in this area. The second dataset7 prepared
by Pang and Lee (2004), which includes 1250 positive reviews, 1250 negative reviews,
and 1250 neutral reviews. The Third one is Blitzer (Blitzer et al. 2007),8 which includes
1000 positive movie review and 1000 negative movie reviews. Table 1 shows the obtained
accuracies of different researches on the benchmark datasets.
2. Opinion identification All the comments should be separated and identified from the
presented texts in this phase. Then the extracted comments should be processed to separate
the inappropriate and fake ones. What we mean by opinions is all the phrases representing
the individual emotions about the products, services or any other desired category.
3. Aspect extraction In this phase, all the existing aspects are identified and extracted
according to the procedures. Selecting the potential aspects could be very effective in
improving the classification.
4. Opinion classification After opinion identification and aspect extraction which can be
considered as the preprocessing phase, in this step the opinions are classified using
different techniques which this paper summarizes, classifies and compares them.
5. Production summary Based on the results of the previous steps, in the production
summary level, a summary of the opinion results is produced which can be in different
forms such as text, charts etc.
6. Evaluation the performance of opinion classification can be evaluated using four evaluation parameters, namely accuracy, precision, recall and f-score.
2 https://www.facebook.com/.
3 http://www.amazon.com/.
4 http://www.yelp.com/.
5 http://www.tripadvisor.com/.
6 http://www.cs.cornell.edu/people/pabo/movie-review-data/.
7 http://www.cs.cornell.edu/people/pabo/movie-review-data/ (review corpus version 2.0).
8 http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.
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Table 1 Obtained accuracies on the benchmark datasets
Datasets Papers Accuracy (%)
Pang et al. (2002) Chen et al. (2011) 64
Li and Liu (2012) 77
Pang and Lee (2004) Penalver-Martinez et al. (2014) 89.6
Fernández-Gavilanes et al. (2016) 69.95
Fersini et al. (2016) 81.7
Saleh et al. (2011) 85.35
Boiy and Moens (2009) 87.40
Blitzer et al. (2007) Xia et al. (2016) 80
Xia et al. (2011) 85.58
Poria et al. (2014) 87
2.3 Opinion mining applications
Sentiment analysis tries to describe and assess the expressed sentiments about the issues
of interest to web users which have been mentioned in textual messages. These issues can
include a range of brands or goods up to the broader favorite topics such as social, political,
economic and cultural affairs. We note to the several major applications of the opinion mining
in this section.
2.3.1 Opinion mining in the commercial product areas
The usage of opinion mining in the area of commercial products (Chen et al. 2014; MarreseTaylor et al. 2014; Jeyapriya and Selvi 2015; Li et al. 2012; Luo et al. 2015) is important
from three viewpoints:
1. The individual customers’ point of view: when someone wants to buy a product, having
a summary of the others’ opinions can be more useful than studying the massive amounts
of others’ comments about this product. Moreover, the customer will be able to compare
the products easily by having a summary of the opinions.
2. The business organizations and producers’ point of view: this issue is important for
the organizations to improve their products. This information is used not only for the
product marketing and evaluation but also for product design and development. The
manufacturing companies can even increase, decrease or change the products based on
customer’s opinions.
3. The advertising companies’ point of view: the opinions are important for advertising
companies because they can obtain ideas of the market demand. The public perspective
of the people and type of products that they are interested in can be found among the
items that extracted by opinion mining.
The important achievements of opinion mining in the commercial products are as follows
(Tang et al. 2009):
• Products comparison Online sellers want their customers to comment about the purchased products. Due to the increasing use of the online marketing and such web services,
these sentiments are growing. These sentiments are useful both for product manufacturers and consumers because they can have a better decision making by comparing the
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sentiments and ideas of others on this product. More researches have been carried out in
this area, which have focused on the issue of automatic classification of the products in
two categories of recommended and non-recommended.
• Sentiments summarization When the number of sentiments increase, its recognition
is difficult either for producers or consumers. With the sentiments summarization, customers find out easier the sentiments of other customers about the product and also
manufacturers realize easier to the customers’ sentiments about the products as well.
• Exploring the reason of opinion The reason of the user to give an opinion can also be
extracted in the opinion mining process. It is extremely important to determine the reason
why consumers like or dislike the product.
2.3.2 Opinion mining in the politics area
Along with the comments on the sale and purchase of goods, with the widespread and
comprehensive use of the Internet services by people, users can also comment on various
political, social, religious, and cultural issues. Collecting and analyzing these comments helps
greatly to politicians, managers of social issues or religious and cultural activists to take
appropriate decisions for improving the social life of the community. One of the significant
applications among these areas is in the political elections that individuals can benefit from
the sentiments of others to make decision in their voting. Analyzing opinions existed in social
networks related to election is addressed in (Tsakalidis et al. 2015; Unankard et al. 2014;
Kagan et al. 2015; Mohammad et al. 2015; Archambault et al. 2013).
2.3.3 Opinion mining in the stock market and stock forecast
Achieving sustained and long-term economic growth requires optimal allocation of resources
at the national economy level and this is not easily possible without the help of appropriate
information and knowledge. Investing in supplied stocks in the stock exchange is one of
the profitable options in the capital market which plays an important role in the individuals’
better decision making and having its own particular audience which predicting the stock.
Among the studies representing the application of the opinion mining in the stock market it
can be pointed out to (Bollen et al. 2011; Nofer and Hinz 2015; Bing et al. 2014; Fortuny
et al. 2014) that the opinions have been used to predict the stock market. For example, Daily
comments of Twitter have been analyzed using OpinionFinder and GPOMS as two important
moods tracking tools by Bollen et al. (2011) and showed the correlation to daily changes in
Dow Jones Industrial Average closing values.
3 Levels of opinion mining
As shown in Fig. 3, opinion mining is possible on four different levels, namely document
level, sentence level, aspect level, and concept level.
Document level (Moraes et al. 2013) of opinion mining is the most abstract level of
sentiment analysis and so is not appropriate for precise evaluations. The result of this level of
analysis is usually general information about the documents polarity which cannot be very
accurate. Sentence level opinion mining (Marcheggiani et al. 2014) is a fine-grain analysis
that could be more accurate. Since the polarity of the sentences of an opinion does not imply
the same polarity for the whole of opinion necessarily, aspect level of opinion mining (Xia
et al. 2015) have been considered by researchers as the third level of opinion mining and
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1502 F. Hemmatian, M. K. Sohrabi
Opinion Mining Levels
Document Level Sentence Level Aspect Level Concept Level
Fig. 3 Different levels of opinion mining
sentiment analysis. Concept level opinion mining is the forth level of sentiment analysis
which focuses on the semantic analysis of the text and analyzes the concepts which do not
explicitly express any emotion (Poria et al. 2014). Several recent surveys and reviews on
sentiment analysis consider these levels of opinion mining from this point of view (Medhat
et al. 2014; Ravi and Ravi 2015; Balazs and Velasquez 2016; Yan et al. 2017; Sun et al. 2017;
Lo et al. 2017).
3.1 Document level
The sentiment analysis may be used in the document level. In this level of the opinion
mining, sentiments are ultimately summarized on the whole of the document as positive or
negative (Pang et al. 2002). The purpose of categorizing comments at the document level is
the automatic classification of information based on a single topic, which is expressed as a
positive or negative sentiment (Moraes et al. 2013). Since this level of opinion mining does
not enter into details and the review process takes place in an abstract and general view, the
mining process can be done much faster. In early works, most of the researches conducted
at the document level and focused on datasets such as the news and the products review.
By increase in the popularity of the social networks, different types of datasets were created
which made increasing the studies of this level (Habernal et al. 2015; Gupta et al. 2015).
Since the entire document is considered as a single entity in document level opinion mining,
this level of opinion mining is not suitable for precise evaluation and comparison. Most of
the techniques carrying out in the opinion classification at this level are based on supervised
learning methods (Liu and Zhang 2012).
3.2 Sentence level
Since, document level sentiment analysis is too coarse, researchers investigated approaches
to focus on the sentence (Wilson et al. 2005; Marcheggiani et al. 2014; Yang and Cardie
2014; Appel et al. 2016). The goal of this level of opinion mining is to classify opinions in
each sentence. Sentiments analysis on the sentence level constitutes of two following steps
(Liu and Zhang 2012):
• Firstly it is determined that the sentence is subjective or objective.
• Secondly the polarity (positive or negative) of sentence is determined.
In the classification of comments at the sentence level, since the documents are broken into
several sentences, they provide more accurate information on the polarity of the views and
naturally entail more challenges than the level of the document.
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3.3 Aspect level
Although the classification of text sentiments on the document and sentence level is helpful
in many cases but it does not provide all the necessary details. For example, being positive
of the sentiments on a document in relation to a particular entity, does not imply that the
author’s opinion is positive about all the aspects of an entity. Similarly, negative sentiments
do not represent the author negative opinion about all the aspects of an entity (Liu and
Zhang 2012). The classification on the document level (Moraes et al. 2013) and sentence
level (Marcheggiani et al. 2014) does not provide these kinds of information and we need to
perform opinion mining in aspect level (Xia et al. 2015) to achieve these details. When the
considered comment does not include a single entity or aspect, this level of opinion mining
is the appropriate option, which is an important advantage of this level of classification and
distinguishes it from the two previous levels. Aspect level opinion mining actually considers
the given opinion itself instead of looking to the language structures (document, sentence or
phrase) (Liu 2012). The objective of this level is to identify and extract the aspects from the
sentiments text and then specify their polarity. This level of sentiments analysis can produce
a summary of the sentiments about different aspects of the desired entity. It can be seen that
this level of opinion mining provides a more accurate result (Chinsha and Joseph 2015).
3.4 Concept level
Cambria (2013) introduced the concept level opinion mining as a deep understanding of the
natural language texts by the machine, in which, the opinion methods should go beyond the
surface level analysis. Cambria et al. (2013) has also presented the concept level of opinion
mining as a new avenue in the sentiment analysis. The analysis of emotions at the concept level
is based on the inference of conceptual information about emotion and sentiment associated
with natural language. Conceptual approaches focus on the semantic analysis of the text
and analyze the concepts which do not explicitly express any emotion (Poria et al. 2014).
An enhanced version of SenteicNet have been proposed in Poria et al. (2013), which assign
emotion labels to carry out concept level opinion mining. Poria et al. (2014) have proposed a
new approach to improve the accuracy of polarity detection. An analysis of comments at the
conceptual level has been introduced that integrates linguistic, common-sense computing, and
machine learning techniques. Their results indicate that the proposed method has a desirable
accuracy and better than common statistical methods. A concept level sentiment dictionary
has been built in Tsai et al. (2013) based on common-sense knowledge using a two phase
method which integrates iterative regression and random walk with in-link normalization. A
concept level sentiment analysis system has been presented in Mudinas et al. (2012), which
combined lexicon-based and learning based approaches for concept mining from opinions.
EventSensor system is represented in Shah et al. (2016) to extract concept tags from visual
contents and textual meta data in concept-level sentiment analysis.
4 Aspect extraction
One of the main important steps in sentiments classification is aspect extraction (Rana and
Cheah 2016). In this section we categorize current techniques for aspect extraction and
selection. As it mentioned, aspect level classification has better performance and a prerequisite
for using it is obtaining aspects. Most researches in the field of aspect extraction have been
focused on the online reviews (Hu and Liu 2004; Li et al. 2015; Lv et al. 2017). In general,
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1504 F. Hemmatian, M. K. Sohrabi
Aspect Extraction Techniques
Based on exploiting opinion
and aspect relations
Based on nouns and the
frequent noun phrases
Based on topic
modeling
Based on the supervised
learning techniques
Fig. 4 Classification of aspect extraction techniques
as is shown in Fig. 4, the related techniques can be placed in four categories (Liu 2012):
Extraction based on the frequent noun phrases and nouns (Jeyapriya and Selvi 2015; Hu and
Liu 2004; Li et al. 2015), Extraction based on exploiting opinion and aspect relations (Qiu
et al. 2011; Wu et al. 2009), Extraction based on the supervised learning (Jin et al. 2009;
Yu et al. 2011), Extraction based on topic modeling (Vuli´c et al. 2015; Mukherjee and Liu
2012).
4.1 Extraction based on frequency of noun phrases and nouns
This method is known as a simple and effective approach. Generally, when people express
their comments about various aspects of a product, they basically use similar words frequently
to express their sentiments (Liu 2012). In this method, the nouns and noun phrases are
determined by a POS tagger and the names that have been frequently repeated are selected
as aspect. POS tags indicate the role of the words in a sentence (Wang et al. 2015a). A list of
POS tags has been collected in Table 2 which shows all the POS tags based on (Liu 2012).
Li et al. (2015) suggested a method for improving feature extraction performance by
online reviews. Their method which is based on frequent noun and noun phrase is consisted
of three important components: frequent based mining and pruning, order based filtering,
and similarity based filtering. Their experimental results show that proposed method could
be generalized over various domains with different-sized data. Jeyapriya and Selvi (2015)
suggested a feature extraction system in product review. They extracted nouns and noun
phrase from each review sentence and used minimum support threshold to find frequent
features in review sentences. Their accuracy was about 80.32
4.2 Extraction based on relation exploitation between opinion words and aspects
This method uses the existing relationship between the aspects and opinion words in the
expressed opinions. Some of the infrequent aspects can be identified with the help of this
method. The main idea is that the opinion words can be used to describe the different
aspects (Liu and Zhang 2012). Qiu et al. (2011) focused on two fundamental and important
issues, opinion lexicon expansion and target extraction, and suggested the double propagation
approach. They performed extraction based on syntactic relations that cause link between
review words and targets. Relations could be detected according to a dependency parser and
then could be used for opinion lexicon expansion and extraction. Results show that their
approach is significantly better than existing methods.
4.3 Extraction based on the supervised learning
Supervised learning approaches are promising techniques for aspect extraction which generates a model of aspects by using labeled data. Support vector machine (SVM) (Cortes and
Vapnik 1995; Manek et al. 2017), conditional random fields (CRF) (Lafferty et al. 2001), and
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A survey on classification techniques for opinion mining… 1505 Table 2 POS tags (Liu 2012) Description Tag Description Tag Description Tag Adjective JJ Comparative adjectives JJR Superlative adjectives JJS Adverb RB Comparative adverb RBR Superlative adverb RBS Noun, plural noun, singular NNS noun, singular or mass NN Comparative adjective JJR Coordinating conjunction CC Subordination conjunction IN Interjection UH Determiner DT Verb, gerund or present participle VBG List item marker LS Model verb MV Cardinal number CD Adjective JJ Verb, past participle VBN Verb, gerund or present participle VBG Particle RP Verb, past tense VBD Personal pronoun PP Possessive ending POS Verb, non-3rd-person singular/p VBP Verb, 3rd-person singular present VBZ Proper noun, plural NPS Proper noun, singular NP Symbol SYM Verb, base form VB
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1506 F. Hemmatian, M. K. Sohrabi
hidden Markov model (HMM) (Rabiner 1989) are some of the supervised learning methods
that can be used to extract the aspects (Liu 2012). In Kobayashi et al. (2007), aspects was
extracted from a collection of blog posts using machine learning methods and the results was
used as statistical patterns for aspect extraction.
4.3.1 Extraction based on topic modeling
In recent years the statistical topic models are considered as a systematic approach to detect
the topics from the text document collections (Vuli´c et al. 2015; Mukherjee and Liu 2012; Liu
2012). Topic modeling is an unsupervised method for aspect extraction in which it assumes
that any document contains k hidden topics. For example in hotel investigation, some standard
features such as location, cleanliness and so on are discussed. Now it is possible that there are
comments about the quality of internet connection, so we are facing with a hidden topic. In
these situations, there is a need for a model to automatically extract relevant aspects without
human supervision (Titov and McDonald 2008). Since this approach, uses statistical methods
like latent semantic analysis (LSA) (Hofmann 1999) and latent Dirichlet allocation (LDA)
(Blei et al. 2003), it is called statistical models too. Also LDA and LSA use the bag of words
represented in documents, so they can be used only in document level opinion mining. Ma
et al. (2015) proposed an approach of probabilistic topic model based on LDA in order to
semantic search over citizens opinions about city issues on online platforms. Their results
show that systems based on LDA provide useful information about their staff members. Luo
et al. (2015) worked on detection and rating feature in product review which is as important
task of opinion mining in aspect level. They presented Quad-tuple PLSA for solving this
problem because entity and rating rarely considered in previous researches hence had great
performance.
5 Opinion classification techniques
Most important and critical step of opinion mining is selecting an appropriate technique to
classify the sentiments. In this section we explain, categorize, summarize and compare proposed techniques in this area. The classification methods which are proposed in the literature
can be fall into two groups: machine learning and lexicon-based approaches. This type of
categorization can be seen in some works (Wang et al. 2015a; Petz et al. 2015), but in this
paper we address the issue much more comprehensive in more details with better comparison
factors and discussions based on the major validated scientific works.
5.1 Machine learning method
According to Fig. 5 three techniques of the machine learning methods are used to classify
the sentiments: supervised, semi-supervised, and unsupervised learning methods.
5.1.1 Supervised learning method
In the supervised learning is, the process of learning is carried out using the data of a training
set in which, the output value is specified for any input and the system tries to learn a function,
by mapping the input to the output, i.e., to guess the relationship between input and output. In
this method, the categories are initially specified and any of the training data is assigned to a
specific category. In fact in the supervised approach, the classifier categorizes the sentiments
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A survey on classification techniques for opinion mining… 1507
Machine Learning Methods
Unsupervised
Learning
Semi-supervised
Learning
Supervised
Learning
Fig. 5 Machine learning based opinion classification techniques
Supervised Learning
Probabilistic
Classification
Non-probabilistic
Classification
Naïve
Bayes
Bayesian
Network
Maximum
Entropy
Support Vector
Machine
Neural
Network
K-Nearest
Neighbour
Decision
Tree
Rulebased
Fig. 6 Supervised learning-based opinion classification methods
using labeled text samples. As shown in Fig. 6, supervised sentiment classification approaches
can be divided into two main categories: Probabilistic Classification and Non-probabilistic
Classification.
5.1.1.1. Probabilistic classification Probabilistic classification is one of the popular classifications approaches in the field of the machine learning. These methods are derived from
probabilistic models which provide a systematic way for statistical classification in complex
domains such as the natural language. Hence, it has an effective application in the opinion
mining. Naive Bayes, Bayesian network and maximum entropy are some of the well-known
methods in the field of opinion mining which belong to this kind of classification.
Naive Bayes (NB)
This method is a simple and popular approach in the area of text classification. It is assumed
that the existing sentences within the document are subjective which the existence of semantic
orientation of words is definitely a final verdict on the subjectivity of the sentences. The
features are also selected from a set of words within the documents. It is an approach to
text classification that assigns the class c∗ = Arg maxc P (c|d), to a given document d. The
relation 1 is expressed based on the Bayesian theory (Pang et al. 2002).
P(c|d) = p(c)p(d|c)
p(d) (1)
where p(d) plays no role in selecting c∗. To estimate the term p(d|c), Naïve Bayes decomposes it by assuming the fi’s are conditionally independent given d’s class as in relation 2
(Pang et al. 2002).
PN B(c|d) = P(C)
πm
i=1P ( fi|c)
ni(d)
p(d) (2)
where m is the number of features and f is the feature vector.
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