Dictionary Based SVM Feature Selection for Sentiment Classification
DOI:
https://doi.org/10.26438/ijcse/v6i8.603607Keywords:
Sentiment Analysis, Classification, Support Vector Machine, Feature Selection, Part-Of- SpeechAbstract
Sentiment Analysis (SA) is the computational study of opinions, sentiments and emotions expressed in text in order to determine the thoughts of people in the direction of certain objects and facts. The opinions of people have a major influence in our every day decision-making process. In recent days, the people are sharing their opinions in the form of blogs, tweets, face book messages, news groups, comments and reviews. The proposed Dictionary Based Support Vector Machine Feature Selection (DBSVMFS) model extracts sentiment features using Support Vector Machine (SVM) weight method to improve the performance of SA. Different levels of pre-processing methods are applied to reduce the features. A set of sentiment features Adjectives, Adverbs and Verbs are extracted by using WordNet based POS (Part-Of-Speech). Feature selection using SVM weight method is applied to select the most important features. SVM classifier is used for sentiment classification and the experimental results prove the effectiveness of the proposed model by improving sentiment classification accuracy
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