Text sentiment computation for online forums hotspot detection
K. Nirmala Devi, V. Murali Bhaskaran · International Journal of Information and Communication Technology · 2016
The user generated content on the web grows rapidly in this emergent information age. The tremendous growth of content available in forums, blogs, news reports, etc., are having large volume of public opinion information, it is essential to analyse in time and understands the trends of their opinion correctly. The evolutionary changes in technology make use of such information to capture only the user's essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focus on the factual domain rather than the opinion domain. In this paper, we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and support vector machine with PSO SVM-PSO classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naive Bayes, decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.