Abstract:
Since last few years, the trend of social networking is at its peak. People post their personal feelings and thinking about any topic or product for social liking or for marketing. Such posts often get hundreds or thousands of comments and it becomes difficult for a reader to read all of these to assess public opinion. Sometimes one just wants to know common opinion, behavior, trend or thinking discussed there or to determine whether those opinions are positive or negative. Particularly in case of product marketing, the company would like to judge the success of an ad campaign or new product launch or which products or services are popular and what people like or dislike about particular features of a product. In such situations automatic sentiment analysis and opinion mining can help a lot. Hence, in this paper a novel sentiment analysis and opinion mining framework is proposed. This framework utilizes various techniques of computational linguistics to measure sentiment orientation of user's opinion around different entities. The proposed framework is used to perform sentiment analysis and opinion mining of users' posts and comments on social media through a Facebook App. Furthermore a user study is conducted to gauge performance of the proposed framework. The results of this study have shown that the framework is capable of finding opinions of the users and sentiments around those opinions with more than 85 percent accuracy when compared with actual human judges.
