Subjectivity Sentence Level Sentiment Analysis and Classification using Correlation Based Embedded Feature Subset using Machine Learning
Keywords:
Correlation Analysis, Fine Grained, Random Forest Classifier, Sentiment PolarityAbstract
Sentiment analysis with sentence level gains importance, as each sentence has a sentiment polarity word. However, in case of product reviews sometimes the review might be lengthy that describes the product fully. Therefore Correlation Analysis based Random Forest with Subjectivity Sentence level Sentiment Analysis and Classification is proposed here. Machine learning techniques have been included into sentiment classification to increase its accuracy and effectiveness. Here two types of analysis such as (i) Sentiment polarity based model is taken for Subjectivity Sentence level Sentiment Analysis and (ii) Classification with the evaluation measures and the proposed method CARF-SSSAC proves its efficiency for Fine Grained and Sentiment polarity model. From the analysis it is proved that the Sentiment Polarity model gives highest accuracy 88.72% for the data range 5000.
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