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Multi-Objective Model Selection (MOMS)-based Semi-Supervised Framework for Sentiment Analysis

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Abstract

Sentiment analysis has emerged as an active research field due to the rapid growth of user-generated content on the Internet. This research area analyzes the opinions and attitudes of masses toward products, movies, topics, individuals, and services. Various machine learning and text mining algorithms have been used for sentiment analysis and classification. The recent research concludes that domain-specific lexicons perform significantly better as compared to domain-independent lexicons. The proposed research aims at improving the performance of general-purpose lexicons utilizing machine learning algorithms. A semi-supervised framework based on “MOMS” is introduced in order to determine the feature weight by incorporating SentiWordNet, a well-known general-purpose sentiment lexicon. The feature weights are learned by support vector machine, and the classification performance is enhanced by using Multi-Objective Model Selection procedure. Subjectivity criterion is used to select the desired features, and the effects of feature selection with respect to their part-of-speech information are studied comprehensively. Experimental evaluation is performed on seven different benchmark datasets which includes Large movie review dataset, Multi-domain sentiment dataset, and Cornell movie review dataset. The comparison of the proposed approach is performed with state-of-the-art techniques, lexicon-based approaches, and other methods for sentiment analysis. The proposed framework results in high performance when compared to other research in this field.

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Notes

  1. https://wordnet.princeton.edu/ [Last Accessed: July 27, 2015].

  2. https://www.jspell.com/java-spell-checker.html [Last Accessed: July 27, 2015].

  3. http://nlp.stanford.edu/IR-book/html/htmledition/dropping-common-terms-stop-words-1.html [Last Accessed: July 27, 2015].

  4. http://www.interopia.com/education/all-question-words-in-english/ [Last Accessed: July 27, 2015].

  5. http://www.noslang.com/dictionary [Last Accessed: July 27, 2015].

  6. http://nlp.stanford.edu/software/tagger.shtml [Last Accessed: July 28, 2015].

  7. www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html [Last Accessed: July 28, 2015].

  8. http://download.joachims.org/svm_light/current/svm_light_windows64.zip [Last Accessed: August 9, 2015].

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Authors and Affiliations

  1. Computer Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Farhan Hassan Khan, Usman Qamar & Saba Bashir

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  1. Farhan Hassan Khan
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Correspondence to Farhan Hassan Khan.

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Farhan Hassan Khan, Usman Qamar, and Saba Bashir declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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Khan, F.H., Qamar, U. & Bashir, S. Multi-Objective Model Selection (MOMS)-based Semi-Supervised Framework for Sentiment Analysis. Cogn Comput 8, 614–628 (2016). https://doi.org/10.1007/s12559-016-9386-8

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