The monitoring of the public sentiment is a popular issue in the study of social media where a myriad of researches concentrate on the general trend of public sentiment towards certain event. However, few of them has analyzed the public sentiment towards various topics on the event. This paper focuses a topic-oriented sentiment analysis on temporal term. And the Weibo on the event of ‘regular’ odd-even ‘vehicle restriction in Beijing’ is selected as the target of our work. By observing the sentimental trend of the different topics on this event, we attempt to offer feasible suggestions for public sentiment monitoring.

The chain-structured long shortterm memory (LSTM) has been shown to be effective in a wide range of tasks such as language modeling, machine translation and speech recognition. Because it cannot storage the structure of hierarchical information language, we extend it to a tree-structure based recursive neural network to capture more syntactic and semantic information, as well as the sentiment polarity shifting. Compared to LSTM, RNN etc, the proposed model achieves a state-of-the-art performance.

This paper presents a fuzzy inference machine for Chinese subjectivity identification. We first define two fuzzy sets for lexical subjectivity and objectivity, respectively. Then, we apply TF-IDF to acquire the relevant membership functions from the training data. Finally, we define two fuzzy IF-THEN rules and thus build a fuzzy inference machine for Chinese subjective sentence recognition. We conduct two experiments on the NTCIR-6 Chinese opinion data. The experimental results demonstrate the feasibility of the proposed method.