A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

    Kumar Ravi
    Kumar Ravi

    Knowledge-Based Systems, Volume 89, 2015.

    Cited by: 472|Bibtex|Views24|Links
    EI
    Keywords:
    Information RetrievalSemeval Affective TextRestaurant ReviewsConditional Random FieldFlesch- Kincaid Grade LevelMore(111+)
    Wei bo:
    There is an urgent need to focus on several other issues raised in currently published papers, which were not the part of the extant surveys. This survey work differs from existing literature surveys in various ways we classified existing studies on the basis of opinion mining ta...

    Abstract:

    Ever increasing use of Internet and online activities (like chatting, conferencing, surveillances, ticket booking, online transactions, e-commerce, social media communications, blogging and micro-blogging, clicks streams, etc.) leads us to extract, transform, load, and analyze very huge amount of structured and unstructured data, at a fas...More

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    Data:

    Introduction
    • Online media and social networking sites (SNS) are used to express and share public experiences in the form of product reviews, blogs, and discussion forums.
    • These media contain highly unstructured data combining text, images, animations and videos that are useful in making public aware of various issues.
    • They highlighted some approaches like similarity dependent, NB classifier, Multiple NB classifier, and cut-based classifier
    Highlights
    • Online media and social networking sites (SNS) are used to express and share public experiences in the form of product reviews, blogs, and discussion forums
    • There is an urgent need to focus on several other issues raised in currently published papers, which were not the part of the extant surveys. This survey work differs from existing literature surveys in various ways (i) we classified existing studies on the basis of opinion mining tasks, approaches and applications as presented in Figure 1, this paper presents articles related to tasks and major issues pointed out by existing articles like subjectivity classification, sentiment classification from coarse-grained to fine-grained level, review usefulness measurement, opinion spam detection, lexicon creation, and opinion word and product aspect extraction as presented throughout the paper we summarized each of surveyed articles in four aspects viz. problem addressed, exploited dataset details, feature representation and selection method, techniques applied, obtained results, and indicated future directions along with our views, we included some recently proposed feature selection techniques for Sentiment analysis, (v) we provided a detailed list of online available datasets, classification of articles on the basis of Sentiment analysis performed at various granular levels as presented in Table 1, the exploited lexica are listed in Table 10, and summary of one hundred and sixty one articles is presented in Table 10 before concluding the paper
    • This paper presents a comprehensive, state-of-the-art review on the research work done in various aspects of Sentiment analysis during 2002-2014
    • The paper is reviewed in six broad dimensions viz. subjectivity classification, sentiment classification, review usefulness measurement, lexicon creation, opinion word and product aspect extraction, and various applications of opinion mining
    • Apart from Support Vector Machine, Neural Network and lexicon based approaches; we found that some of the intelligent techniques have not been exploited exhaustively like random forest, evolutionary computation, association rule mining, fuzzy rule based systems, rule miner, conditional random field theory (CRF), formal concept analysis, radial basis function neural network (RBFNN), and online learning algorithms
    • Sentiment classification system and achieved up to 86% F1
    • Sentiment is often found in vague form, fuzzy logic is eminently suitable to model the vagueness in more robust way
    Methods
    • Empirical Methods in Natural Language

      Processing (EMNLP 02), Assoc. for Computational Linguistics, 2002, pp. 214–221. [84] A.
    • Processing (EMNLP 02), Assoc.
    • Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments, Decision Support Systems 53 (2012) 675–679.
    • [85] F.H. Khan et al, TOM: Twitter opinion mining framework using hybrid classification scheme, Decision Support Systems (2013), http://dx.doi.org/10.1016/j.dss.2013.09.004.
    • Rosso, Making objective decisions from subjective data: Detecting irony in customer reviews, Decision Support Systems.
    • Processing (EMNLP 2003), 2003, pp.
    • SenticNet: A Publicly Available Semantic Resource for Opinion Mining, In AAAI
    Results
    • EWGA improved the classification accuracy up to 92.84% on English dataset, and 93.84% on Arabic dataset.
    • Experiments are carried out on the dataset of Blitzer et al [149] and 3345 reviews on apparel and beauty from amazon.com
    • They reported F1 up to 88.58% for entity detection and an accuracy of 83.33% for identifying product-referring terms using RBC, where RBC outperformed DT and SVM.
    • The proposed ontology quality was compared against Text-to-Onto based ontology
    • They reported 11.6% better sentiment classification accuracy than that obtained by OpinionFinder.
    Conclusion
    • This study reviews many interesting and useful works regarding the state-of-the-art in SA.
    • Subjectivity classification, sentiment classification, review usefulness measurement, lexicon creation, opinion word and product aspect extraction, and various applications of opinion mining.
    • These six dimensions refer to tasks to be accomplished for SA.
    • Ontology can be useful in globalizing the measurement standard of sentiments
    Summary
    • Introduction:

      Online media and social networking sites (SNS) are used to express and share public experiences in the form of product reviews, blogs, and discussion forums.
    • These media contain highly unstructured data combining text, images, animations and videos that are useful in making public aware of various issues.
    • They highlighted some approaches like similarity dependent, NB classifier, Multiple NB classifier, and cut-based classifier
    • Methods:

      Empirical Methods in Natural Language

      Processing (EMNLP 02), Assoc. for Computational Linguistics, 2002, pp. 214–221. [84] A.
    • Processing (EMNLP 02), Assoc.
    • Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments, Decision Support Systems 53 (2012) 675–679.
    • [85] F.H. Khan et al, TOM: Twitter opinion mining framework using hybrid classification scheme, Decision Support Systems (2013), http://dx.doi.org/10.1016/j.dss.2013.09.004.
    • Rosso, Making objective decisions from subjective data: Detecting irony in customer reviews, Decision Support Systems.
    • Processing (EMNLP 2003), 2003, pp.
    • SenticNet: A Publicly Available Semantic Resource for Opinion Mining, In AAAI
    • Results:

      EWGA improved the classification accuracy up to 92.84% on English dataset, and 93.84% on Arabic dataset.
    • Experiments are carried out on the dataset of Blitzer et al [149] and 3345 reviews on apparel and beauty from amazon.com
    • They reported F1 up to 88.58% for entity detection and an accuracy of 83.33% for identifying product-referring terms using RBC, where RBC outperformed DT and SVM.
    • The proposed ontology quality was compared against Text-to-Onto based ontology
    • They reported 11.6% better sentiment classification accuracy than that obtained by OpinionFinder.
    • Conclusion:

      This study reviews many interesting and useful works regarding the state-of-the-art in SA.
    • Subjectivity classification, sentiment classification, review usefulness measurement, lexicon creation, opinion word and product aspect extraction, and various applications of opinion mining.
    • These six dimensions refer to tasks to be accomplished for SA.
    • Ontology can be useful in globalizing the measurement standard of sentiments
    Tables
    • Table1: Distribution of articles based on the granularity of sentiment analysis
    • Table2: Available tools for text preprocessing
    • Table3: Distribution of articles based on tasks and applications
    • Table4: Subjectivity classification accuracy reported on common datasets
    • Table5: Sentiment classification accuracy reported on common datasets
    • Table6: Distribution of articles based intelligent techniques applied
    • Table7: Year wise distribution of articles
    • Table8: Number of articles published (and reviewed here) in different journals
    • Table9: List of publicly available datasets
    • Table10: Summary of reviewed articles
    Download tables as Excel
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