An Approach for Sentiment Analysis of Customer Review Data

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Deevi Radha Rani, Ongole Gandhi, Viswandapalli Anusha, Shaik Shabbir Hussain, Divya Vadlamudi


Sentiment analysis is more promising tool to select the best product based on the customer reviews. The increase in number of websites and brands who are advertising their products leads to increase of customer reviews day by day. Manually it is not feasible to analyze and decide the opinion against a product using those huge reviews. Sentiment analysis automates the process of classifying the products as positive, negative and neutral based on customer reviews. This paper focus on performing sentiment analysis on the text data that contains the customer reviews to obtain the sentiment i.e., opinion of the user about the product from the reviews that the customer have given. This paper also presents the classification of sentiment analysis techniques and stages in sentiment analysis. The approach used in this paper uses both lexicon based technique and machine learning technique, especially SVM. Performance of our proposed approach is evaluated using precision, recall and F1-score. The accuracy of different decisions is also calculated. We used kaggle dataset for the experimenting our proposed sentiment analysis approach.

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