Description

With the rapid development of Internet technology and socialization, people are increasingly accustomed to express their feelings and emotions online. Therefore, emotional information has been aggressively distributed in a variety of social Medias, such as product reviews, news comments, microblogs, social networks, etc. However, facing the massive emotional data, people cannot get any overall impression without sentiment extracting and analyzing. Sentiment extraction and analysis in this type of content not only give an emotional snapshot of the online world but also have potential commercial and sociological values for individuals, merchants and even the governments. Visualization as one of the most efficient sentiment analysis measures provides an intuitive way to exam and analyze the results of auto sentiment classification, which is no longer a passive process that produces images from a set of numbers. Sentiment visualization on tweet topics has recently gained attentions due to its ability to efficiently analyze and understand the people’s feelings for individuals and companies. In the existing system, Cross-domain sentiment classification is challenging and many works proposed their solutions. In this project the system proposed a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics. The algorithm iteratively minimizing the margins of two independent objectives separately on text and non-text features to learn coefficient matrices. To better evaluate our algorithm, the system test it with some different ratios for randomly sampling training data, and controls parameter settings.