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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 307-312 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.525

Content-Specific Unigrams and Syntactic Phrases to Enhance Senti Word Net Based Sentiment Classification

Muhammad Latif, Usman Qamar, and Abdul Wahab Muzaffar
Abstract—Sentiment classification intelligently detects the polarity of documents by ascertaining polar values encapsulated in the document to classify them into positive and negative sentiments. Machine learning classifier completely relies on the feature set orientations. SentiWordNet is a lexical resource where each term is associated with numerical scores for subjective and objective sentiment information. SentiWordNet based sentiment classifier uses sentiment features generated from 7% subjective terms available in the resource. Sentiment features bear generic orientation for multiple domains but lacks comprehensive coverage e.g. Text unit with null or few sentiment features reflects ambiguous or null sentiments. Use of content specific unigrams and syntactic phrases along with sentiment features ensures consistency in the classification while enhancing the performance paradigm. Model proposed in this research is validated on sentiment and polarity datasets. Results of this research, completely out performs previous approaches and methods.

Index Terms—Content specific features, lexicon based classification, sentiment classification, Senti word net.

The authors are with the National University of Sciences and Technology, Islamabad, Pakistan (e-mail: latif_awang001@yahoo.com, usmanq@ceme.nust.edu.pk, wahab.muz@ceme.nust.edu.pk).

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Cite: Muhammad Latif, Usman Qamar, and Abdul Wahab Muzaffar, "Content-Specific Unigrams and Syntactic Phrases to Enhance Senti Word Net Based Sentiment Classification," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 307-312, 2015.

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