ENHANCED CROSS DOMAIN SENTIMENTAL CLASSIFICATION USING PATTERN RECOGNITION

Millions of users share their opinions on Social Networks, making it a valuable platform for tracking and analyzing public sentiment. Such tracking and analysis can provide critical information for decision making in various domains. Therefore it has attracted attention in both academia and industry. Previous research mainly focused on modelling and tracking public sentiment. In this work, we move one step further to interpret sentiment variations. We observed that emerging topics (named foreground topics) within the sentiment variation periods are highly related to the genuine reasons behind the variations.Proposed work that tries to analyze and interpret the public sentiment variations in micro blogging services. Two novel generative models are developed to solve the reason mining problem. The two proposed models are general: they can be applied to other tasks such as finding topic differences between two sets of documents. We propose a sentimental data analysis model using Neural Networks. Both positive and negative feed backs will be calculated here. These foreground topics can give potential interpretations of the sentiment variations. To further enhance the readability of the mined reasons, we select the most representative tweets for foreground topics and develop another generative model called Reason Candidate and Background LDA (RCB-LDA) to rank them with respect to their popularity within the variation period.