[Objective] This paper analyzes online reviews to identify the patterns of their topic contents and sentiments. [Methods] First, we obtained the sentiment of the reviews with the SSTM model. Then, we proposed a DSTM model based on the document, document sentiment distribution and words. Finally, we estimated the distribution of sentiment-topic and the keywords. [Results] We modeled the review datasets by time slice and found the changing trends of contents and sentiments over time. [Limitations] The proposed model did not include the relationship among different subjects, which might generate errors. [Conclusions] The DSTM model, which integrates the external time features, can effectively analyze the evolution of online review topics.
Somprasertsri G, Lalitrojwong P.Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization[J]. Journal of Universal Computer Science, 2010, 16(6): 938-955.
doi: 10.3217/jucs-016-06-0938
[2]
Zhuang L, Jing F, Zhu X Y.Movie Review Mining and Summarization[C]// Proceedings of the 15th ACM International Conference on Information and Knowledge Management. ACM, 2006: 43-50.
[3]
Hu M, Liu B.Mining and Summarizing Customer Reviews[C]// Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA. 2004: 168-177.
[4]
Jo Y, Oh A H.Aspect and Sentiment Unification Model for Online Review Analysis[C]//Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 2011: 815-824.
[5]
Lin C, He Y, Everson R, et al.Weakly Supervised Joint Sentiment-topic Detection from Text[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(6): 1134-1145.
doi: 10.1109/TKDE.2011.48
[6]
Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
(Xiong Shufeng, Ji Donghong.A Short Text Sentiment-topic Model for Product Review Analysis[J]. Acta Automatica Sinica, 2016, 42(8): 1227-1237.)
doi: 10.16383/j.aas.2016.c150591
[8]
Blei D M, Lafferty J D.Dynamic Topic Models[C]// Proceedings of the 23rd International Conference on Machine Learning. 2006: 113-120.
[9]
Griffiths T L, Steyversm M.Finding Scientific Topics[J]. Proceedings of the National Academy of Science of the United States of America, 2004, 101(S1): 5228-5235.
doi: 10.1073/pnas.0307752101
[10]
Alsumaitl L, Barbará D, Domeniconic C.On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking[C]// Proceedings of the 8th IEEE International Conference on Data Mining. 2008.
[11]
Yan X, Guo J, Lan Y, et a1. A Biterm Topic Model for Short Texts[C]//Proceedings of the 22nd International Conference on World Wide Web. 2013.
[12]
Andrzejewski D, Zhu X.Latent Dirichlet Allocation with Topic-in-Set Knowledge[C]// Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing.2009: 43-48.
[13]
Xu H, Zhang F, Wang W.Implicit Feature Identification in Chinese Reviews Using Explicit Topic Mining Model[J]. Knowledge-Based Systems, 2015, 76: 166-175.
doi: 10.1016/j.knosys.2014.12.012
[14]
李实. 中文网络客户评论中的产品特征挖掘方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2009.
[14]
(Li Shi.Research on the Approaches of Mining Product Features from Chinese Customer Reviews on the Internet [D]. Harbin: Harbin Institute of Technology, 2009.)
(Li Chaoxiong, Huang Faliang, Wen Xiaoqian, et al.Evolution Analysis Method of Microblog Topic-Sentiment Based on Dynamic Topic Sentiment Combining Model[J]. Journal of Computer Applications, 2015, 35(10): 2905-2910.)
doi: 10.11772/j.issn.1001-9081.2015.10.2905