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).
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.