|
|
Analyzing Online Reviews with Dynamic Sentiment Topic Model |
Li Hui, Hu Yunfeng() |
School of Economics and Management, Xidian University, Xi’an 710071, China |
|
|
Abstract [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.
|
Received: 07 April 2017
Published: 18 October 2017
|
|
[1] |
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.
|
[7] |
熊蜀峰, 姬东鸿. 面向产品评论分析的短文本情感主题模型[J]. 自动化学报, 2016, 42(8): 1227-1237.
doi: 10.16383/j.aas.2016.c150591
|
[7] |
(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.)
|
[15] |
李超雄, 黄发良, 温肖谦, 等. 基于动态主题情感混合模型的微博主题情感演化分析方法[J]. 计算机应用, 2015, 35(10): 2905-2910.
doi: 10.11772/j.issn.1001-9081.2015.10.2905
|
[15] |
(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
|
[16] |
徐戈, 王厚峰. 自然语言处理中主题模型的发展[J]. 计算机学报, 2011, 34(8): 1423-1436.
|
[16] |
(Xu Ge, Wang Houfeng.The Development of Topic Models in Natural Language Processing[J]. Chinese Journal of Computers, 2011, 34(8): 1423-1436.)
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|