Using text mining and sentiment analysis for online forums hotspot detection and forecast

Text sentiment analysis, also referred to as emotional polarity computation, has become a flourishing frontier in the text mining community. This paper studies online forums hotspot detection and forecast using sentiment analysis and text mining approaches. First, we create an algorithm to automatically analyze the emotional polarity of a text and to obtain a value for each piece of text. Second, this algorithm is combined with K-means clustering and support vector machine (SVM) to develop unsupervised text mining approach. We use the proposed text mining approach to group the forums into various clusters, with the center of each representing a hotspot forum within the current time span. The data sets used in our empirical studies are acquired and formatted from Sina sports forums, which spans a range of 31 different topic forums and 220,053 posts. Experimental results demonstrate that SVM forecasting achieves highly consistent results with K-means clustering. The top 10 hotspot forums listed by SVM forecasting resembles 80% of K-means clustering results. Both SVM and K-means achieve the same results for the top 4 hotspot forums of the year.