Abstract
Problem statement: Online forums hotspot prediction is one of the sig nificant research areas in web mining, which can help people make proper decision in daily life. Online forums, news reports and blogs, are containing large volume of public op inion information. Rapid growth of network arouses much attention on public opinion, it is important t o analyse the public opinion in time and understand s the trends of their opinion correctly. Approach: The sentiment analysis and text mining are importa nt key elements for forecasting the hotspots in online forums. Most of the traditional text mining work o n static data sets, while the online hotspot forecast s works on the web information dynamically and timely. The earlier work on text information processing foc uses in the factual domain rather than opinion domain. Due to the semi structured or unstructured characteristics of online public opinion, we introd uce traditional Vector Space Model (VSM) to express them and then use K-means to perform hotspot detection, then we use J48 classifier to perform ho tspot forecast. Results: The experimentation is conducted by Rapid Miner tool and performance of proposed method J48 is compared with other method, such as Naive Bayes. The consistency between K-means and J48 is validated using three metrics. They are accuracy, sensitivity and specifi city. Conclusion: The experiment helps to identify that K-means and J48 together to predict forums hotspot. The results that have been obtained using J48 present a noticeable consistency with the results a chieved by K-means clustering.
Highlights
Opinion mining is an important sub discipline within data mining and Natural Language Processing (NLP), which automatically extracts, classifies and understands the opinion generated by various users
The sensitivity shows the fraction of forums which are classified by classification algorithm as hotspots among all forums that are labelled by K-means as hotspot
The average sensitivity values obtained for different K values using J48 and Naive Bayes classification is shown in the Table 5
Summary
Opinion mining is an important sub discipline within data mining and Natural Language Processing (NLP), which automatically extracts, classifies and understands the opinion generated by various users These techniques help to enhance the value of existing information resources that can be integrated with new products and systems as they are brought on-line. The company could collect comments to their new products or the marketing department understands the timely requirements of the customers regarding products and services This has motivated on the detection of hotspot as well as prediction of hotspot forums (Li and Wu 2010) where useful information are made available quickly for those customers which might make them benefit in decision making process.
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