| Along with the rapid development of the Internet, network provides people with unprecedentedly open, convenient platform for information sharing and releasing. And more and more people express their opinions, ideas, feelings and attitudes through network, which include positive information boosting the development of events, also include some negative information making the events more badly. At the same time, the openness, directness and concealment of network make it influence the people's ideology more importantly. Therefore, monitoring and analyzing the huge network information timely and effectively has practical significance in maintaining the social stability and promoting the national development.Network public opinion monitoring system is closely related to the Natural Language Processing technology. Because of the limited Natural Language Processing technology, traditional system solves the topic recognition and relevant content of it, but pay less attention to the emotional factor in public opinion. Although some scholars research the opinion mining of public opinion, the close relation between corpus and result makes the low practicability.In recent years, along with the gradually deeper researching of Natural Language Processing, shallow semantic analysis starts to make a figure, and performs more intelligently and practically in related application and research compare to part-of-speech and syntactic analysis. Shallow semantic analysis is a simplified semantic analysis, which represents the meaning of a sentence centering on the verb which is the key to understand the whole meaning. Semantic role labeling is a shallow semantic analysis, which labels some words and expressions'semantic roles for a given verb. It has some advantages such as clearly defined analyzing task, easy to evaluate and etc.Based on the comparative analysis of existing public opinion monitoring algorithms, we design and implement a network public opinion monitoring and analyzing system combing new Natural Language Processing technology, and put forward a novel tendency algorithm which integrating the semantic similarity computing algorithm between words released on HowNet with the tendency computing algorithm based on single character, and also optimize the existing hot topic identification and tracking. Also,based on the statistical analysis of mass samples, we find the regular pattern in tendency texts which is represented as role-feature probability table and role-emotion probability table and provides objective data base for subsequent analysis.This paper mainly includes the following content:(1) The design of system framework and main modules. According to the characteristic of public opinion, this paper designs the system framework and mainly modules which includes the information preprocessing module, information mining module and information service module.(2) The research of hot topic identification and tracking. In order to extract and track the topic appearing with high frequency in a period of time, this paper integrates the ICTCLAS word segmentation, the feature extraction of document frequency, TFIDF weighting computing and K-means clustering algorithm.(3) The research of shallow semantic analysis. This paper uses semantic role labeling tools to label the semantic role of word in texts through training and testing, which can improve the efficiency of text tendency analysis significantly.(4) The research of text tendency analysis. This paper presents methods to extract the feeling and opinion in the texts, which mainly includes emotional lexicon construction, feature lexicon construction and emotional tendency computing algorithm and knowledge discovery in corpus, etc.The related tasks in this paper have applied in domestic events analysis and it can effectively help network public opinion monitoring reduce human intervention. It will play a positive benefit in future network information management. |
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