Text analytics of web posts' comments using sentiment analysis

Social networking Web sites are the most common platform to share and voice one's opinion. The posts and the corresponding comments written on one's wall, such as Facebook assist people in decision making under various situations based on the opinions of other people. Many a times these comments influence one's thought process while making decisions due to higher number of likes and comments viewed on such posts. It has often been observed that users press the like button on any post within fractions of seconds without even completely reading the post. The outcome is though the user likes a particular post but enters a negative comment for it. This paper proposes and implements the sentiment analysis approach to determine the actual popularity of a post on social networking websites. This approach will provide the actual statistics to facilitate the adjudication, that is, if the idea or thought conveyed by the user supports the post or not. The comments are analyzed using a Lexicon based sentiment analysis approach. In this approach, the actual numbers of negative and positive comments are discovered, thus providing the actual statistics and preventing a post from attaining false admiration.