dc.description.abstract | With the development of technology and the growth of data volume, Opinion Mining has become one of the most popular tasks in the field of Information Retrieval in recent years. Regarding the details of Opinion Mining, if we want to understand the semantic tendency of a text, then we must first analyze the semantic intention of each words in the text. According to past studies, there are many methods for Word-level opinion exploration, such as Corpus method, Dictionary method and Seed Set method. This study will mainly focus on Seed Set method. A seed set is a list of words with positive and negative labels. Through the seed set, scholars can carry out more research and application about Opinion Mining. Regarding the shortcomings of traditional ways to construct seed sets, most studies have obtained seed sets by manual selection or citing past research data. However, if today users want to explore other opposing concepts, then it’s hard for them to re-establish a seed set or find the right resources in a short time. In order to solve the problems of the past research, this study will first use the lexical database WordNet to extract words related to the opposing concepts. After extracting, we will introduce the vector tool word2vec to screen out the words with higher similarity to concepts, finally we will use Google Search to retain words with more popularity as a seed set of the opposing concepts. The contribution of this research is to propose an automated approach to select a seed set for any semantically opposite concept, our approach can not only boost the efficiency of seed set selection, but also is not limited to the traditional opposite concepts (positive and negative).
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