| 摘要: |
| 通过主题模型与语义网络对旅游电商中的评论文本进行挖掘，从而引导消费者与商家对评论信息作出重要决策；提出一种基于LDA(Latent Dirichlet Allocation,LDA）主题聚类与语义网络模型(LDA topic clustering and semantic network model，LTC-SNM)的方法对酒店在线评论文本进行研究；获取在线评论文本进行数据预处理，使用Word2vec生成词向量，利用机器学习算法对评论文本进行情感分类；通过LDA主题模型对分类后的文本进行聚类，生成酒店的特征主题词；通过ROSTCM将特征主题词与所修饰的情感词生成语义网络，缓解了挖掘文本信息的复杂性；实验结果表明：提出的LTC-SNM文本挖掘方法使得在线用户评价的主题更具表达性。 |
| 关键词: LDA主题模型 文本聚类 语义关联 情感分析 |
| DOI： |
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| Research on Comment Text Mining Based on LDA Model and Semantic Network |
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WANG Tao,LI Ming
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| Abstract: |
| The topic text and the semantic network are used to mine the comment texts in the travel e-commerce, thereby guiding consumers and businesses to make important decisions on the comment information. This paper proposes a method based on LDA (Latent Dirichlet Allocation,LDA) topic clustering and semantic network model (LDA topic clustering and semantic network model，LTC-SNM) to study the online commentary text of hotels.Firstly, the online review text is obtained for data preprocessing, Word2vec is used to generate the word vector, and the machine learning algorithm is used to classify the comment text. Secondly, the classified text is clustered by the LDA theme model to generate the hotel’s feature keywords. Finally, through ROSTCM, feature subject words and modified emotional words are generated into a semantic network, which alleviates the complexity of mining text information. The experimental results show that the proposed LTC-SNM text mining method makes the topic of online user evaluation more expressive. |
| Key words: LDA topic model text clustering semantic association sentiment analysis |
