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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (9): 74-82    DOI: 10.11925/infotech.2096-3467.2017.09.08
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Analyzing Online Reviews with Dynamic Sentiment Topic Model
Li Hui, Hu Yunfeng()
School of Economics and Management, Xidian University, Xi’an 710071, China
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[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.

Key wordsShort-text Sentiment-Topic Model      Dynamic Sentiment Topic Model      Parameter Estimation      Sentiment Online Reviews     
Received: 07 April 2017      Published: 18 October 2017
ZTFLH:  G350  

Cite this article:

Li Hui,Hu Yunfeng. Analyzing Online Reviews with Dynamic Sentiment Topic Model. Data Analysis and Knowledge Discovery, 2017, 1(9): 74-82.

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参数 具体含义
α 主题θ的先验狄利克雷参数
θ 情感s的主题分布
z 文档中词汇的主题
π 文档的情感分布
s 文档采样的某一情感
φ 主题的词分布
wi 文档中的第i个词汇
E 文档集的情感数量
D 文档子集中的文档数量
W 文档中的词汇数量
K 文档集的主题数量
积极情感 消极情感
主题1 主题2 主题3 主题5 主题6
样子 系统 功能 发热 灵敏
手机 反应 软件 失灵 屏幕
操作 卸载
后盖 四核 配置 充电 触屏
顺手 齐全 不行 分辨率
做工 内存 通话 电池
速度 性价比 字体
漂亮 流畅 像素 充电器
配置 运行 信号 每天 失灵
电源键 性能 毫安
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