| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程學系 === 106 === Network of development gives people some convenience. However, there is a great deal of textual information that we need to read every day, so that we can utilize the opinion exploration to capture the part of the text we are interested in. Usually, people interested in who made comments or opinions in the article, and which are called opinion holders. This study proposes a supervised machine learning method. First we find the opinion of the article, and then identify the author of the article in the opinion and the holder of the opinion.
The method of natural language processing is used to identify the author of the article as well as the opinion holder, in which the method includes tokenization, collecting opinion words, stemming, finding opinion, part-of-speech tagging, recognizing the named entity and the author of the article and the feature extraction. In the feature extraction section, thesis dissertation uses the features of lexical related information, part of speech related information, punctuation related information, named entity related information, syntactic related information, opinion word information and sentence information to identify the article's opinion sentences, author's opinions and opinions holder.
The experimental results show that, the article author's opinion recognition can achieve 69.05% of the F-1 value and the opinion holder extraction can get 72.06% of the F-1 value.
Keywords: opinion exploration, opinion extraction, opinion holder identification, machine learning, supervised learning
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