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

Content Provider	Springer Nature Link
Author	Khan, Farhan Hassan Qamar, Usman Bashir, Saba
Copyright Year	2016
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.
Starting Page	614
Ending Page	628
Page Count	15
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	8
Issue Number	4
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-02-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Text mining Natural language processing Sentiment analysis Feature selection Support vector machine Chi-square Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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