Product rating using sentiment analysis

Customer feedbacks are the mile stones for the success functionality for the companies. A producer will get the correct result of his product from the customer feedback. He can make necessary changes to his product according to the feedback. But most users always fail to give their feedbacks. To avoid the difficulty of providing feedback, this paper focus on the technique of providing automatic feedback on the basis of data collected from Twitter. These data streams are filtered and analyzed and feedback is obtained through opinion mining. Here we mainly analyze the data for mobile phones. The experiments have shown 80% accuracy in the sentimental analysis. Our framework is able to provide fast, valuable feedbacks to companies.

[1]  Ali Selamat,et al.  Sentiment analysis using Support Vector Machine , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[2]  Wan-Sup Cho,et al.  Web Application for Sentiment Analysis Using Supervised Machine Learning , 2015 .

[3]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[4]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[5]  Daniel A. Keim,et al.  Visual opinion analysis of customer feedback data , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[6]  Timothy O'Keefe.  Feature Selection and Weighting Methods in Sentiment Analysis , 2009 .

[7]  Ruwei Dai,et al.  AMAZING: A sentiment mining and retrieval system , 2009, Expert Syst. Appl..

[8]  Brendan T. O'Connor,et al.  Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments , 2010, ACL.

[9]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .