SENTIMENT ANALYSIS FOR ONLINE FORUMS HOTSPOT DETECTION

Abstract

The user generated content on the web grows rapidly in this emergent information age. 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, focuses in the factual domain rather than the opinion domain. Text mining plays a vital role in online forum opinion mining. But opinion mining from online forum is much more difficult than pure text process due to their semi structured characteristics. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyzes the forum text data and computes value for each piece of text. The proposed approach combines K-means clustering and Support Vector Machine (SVM) 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 experiment helps to identify that K-means and SVM together achieve highly consistent results. The prediction result of SVM is also compared with other classifiers such as Naïve Bayes, Decision tree and among them SVM performs the best.

Keywords

• Sentiment Analysis
• Hotspot
• Text Mining
• K-means
• SVM