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Sentiment Analysis technique involves extracting the relevant information from Unstructured User Reviews (UUR) dataset fetched from online and classifying them into appropriate positive and negative comments for making decisions. In UUR, data may be in noisy state, irrelevant features exist which creates high dimensional feature space. To design an effective sentiment learning model, users are required to extract the most relevant sentiment features from UUR. To overcome the issue, we proposed a Linguistic rule based feature selection method for extracting and selecting the sentiment features for Sentiment Analysis as it improves the predictive performance of classification algorithms. The proposed novel feature selection method involves identifying the various sentiment features in the review dataset by using filtering methods such as POS tags, n-grams. In the ensemble model, where the Random Forest classification algorithm is trained for textual sentiment classification, the chosen sentiment feature sets are used. Finally, we test our approach using the real-time review dataset that was collected from a multitude of sources, and the results demonstrate prediction accuracy that is superior to that of existing Sentiment analysis techniques.
Saraswathi et al. (Sat,) studied this question.