Abstract
The analysis of topic evolution mainly refers to the mining of topic content which evolves as the time goes on. With the assumption that topic content may be embodied by key words, this article adopted word2vec for the training of 750,000 pieces of news and micro-blog texts and thus established the model of word vector. Then, the text information flow was applied into the model and all word vectors by time series were acquired. Finally, the word vectors were clustered by K-means before the key words were drawn and the analysis of topic evolution was visualized. By comparing the effect of the model of word vector on drawing topic with those of LDA or PLSA topic models, the results showed that the former is superior to the latter two models. Besides, to collect abundant and varied data will facilitate the training of the model of word vector with better generalization ability and the investigation on real-time analysis of topic evolution.
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Acknowledgment
Supported by The National Social Science Funding Project of China (12BYY045); The Twelfth Five-Year Plan for Philosophy and Social Sciences Project of GuangZhou (15Q16); The Philosophy and Social Sciences Project of GuangDong (GD15YTS01).
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Jianghao, L., Yongmei, Z., Aimin, Y., Jin, C. (2016). Analysis of Topic Evolution on News Comments Based on Word Vectors. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_41
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