关键词: 主题模型, 自编码器, 特征扩展, 比较文本分类

Abstract:

Comparative text is very important for competitive products analysis, but there are few researches on the classification of comparative text in the Q&A field. Aiming at the characteristics of rich information and concentrated topics in Q&A texts, this paper proposes a comparative text classification method based on topic feature and keyword feature expansion. Based on the pretrained topic model, the topic probability distribution of the Q&A text is inferred as its topic feature. In view of the keyword information loss caused by vector concatenation and summation, GRU-autoencoder is designed to realize feature extraction, and the encoder output is used as the keyword feature of Q&A text. Integrating the topic information and keyword semantics, the comparative text features are constructed from the perspectives of linguistics, product, sentiment, social, topic and keyword, then the Q&A text is classified by using various classifiers. The experimental results show that the constructed features are effective and the effect of the classification are better.

Key words: topic model, autoencoder, feature expansion, comparative text classification