IJMTES – HYBRID CLASSIFIER FOR ENHANCING THE POLARITY CLASSIFICATION IN SENTIMENT ANALYSIS

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : HYBRID CLASSIFIER FOR ENHANCING THE POLARITY CLASSIFICATION IN SENTIMENT ANALYSIS

Author’s Name : D Dhanalakshmiunnamed

Volume 04 Issue 03 2017

ISSN no:  2348-3121

Page no: 90-94

Abstract – Sentiment analysis is used for polarity classification of reviews. The reviews are categorized as either positive, negative, or neutral word. Hybrid classifier algorithms used to improve the classification performance. The Structure-based method used to calculate the weight for sentiment text or segments according to the structure of the text. The proposed work improves the polarity classification, which was expensive and had less accuracy. The polarity classification depends on the sentiment information provides additional important features. A rhetorical structural element used for top level and least level feature selection method. Different Classifier used to calculate the good polarity still improves the accuracy and classification performance. To calculate the precision and recall value based on classifier. The high precision and recall value of obtaining sentiment word.

KeywordsSentiment Analysis, Movie Reviews data set, Polarity Classification, Net Beans IDE, Weka, Eclipse, Multi Domain Data set

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