| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 103 === In recent years, various Internet services have been developed and quite popular. People not only retrieve rich information through the Internet but also share their own opinions to the public. The great quantity of personal opinions, which could be used in understanding product evaluation, market trends, and customer feedback, are very valuable to individuals and enterprises. Therefore, the opinion mining technologies that can discover hidden value from a large amount of messages are quite important. Moreover, the person who publishes an opinion is also an essential part of the opinion, especially in social networks. Those personal properties in an opinion are worth being considered when analyzing the social network.
Along with the rise of network technology and social network, microblog service is becoming popular. More and more study of emotion analysis focus on detecting emotions in texts from microblog service. Different from posting a long article, the users of microblog service need to express their thoughts with few and limited words. Therefore, the contents of microblog usually contain network informal language which has non-complete structures. It is more difficult to detect emotions in short sentences of microblogs then in long articles. In this study, an opinion classification and ranking system for Facebook fan pages based on Chinese semantic orientation is proposed, including training and classification parts. In the training part of the system, firstly, every training comment data is tokenized into words by Chinese Lexical Analysis System. Then we create some words combination rules according to term frequency of two words and proper nouns of Facebook fan pages. Finally, the extraction of emotion features in training data is performed. We translate those emotion features into feature vectors, training a Support Vector Machine classification model to classify emotions. In the classification part, the system tokenizes the comments data and performs feature extraction at first. After that, the system classifies emotion of every social comment into positive, neutral or negative based on the Support Vector Machine emotions classification model trained in the training part, and then calculate the comment score according to all the emotion words of that commenter.
In our experiment, the comments from Facebook Fan Pages are employed as resource data to achieve opinion analysis based on users’ characteristics. The adjustment of comment scores can reduce the influences caused by different properties of different users when using emotion words, making the comments score more effectively reflect the emotion degree of the comments.
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