Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (7): 110-117    DOI: 10.11925/infotech.2096-3467.2018.1362
Current Issue | Archive | Adv Search |
Studying Content Interaction Data with Topic Model and Sentiment Analysis
Xu Hongxia,Yu Qianqian(),Qian Li
National Science Library, Chinese Academy of Sciences, Beijing 100190, China;Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Download: PDF (694 KB)   HTML ( 17
Export: BibTeX | EndNote (RIS)      

[Objective] This paper explores data mining techniques for confrontational opinions from interaction data of online community.[Methods] First, we constructed a new algorithm to analyze emotional confrontations based on sentiment analysis and topic model. Then, we included the characteristics of knowledge, topic, and interaction data to the new model. Finally, we conducted an empirical study on the topic of AlphaGo.[Results] There was significant “Pro-AlphaGo” and “Anti-AlphaGo” confrontations online. The “Pro-AlphaGo” topics included human intelligence, competition and ability. The “Anti-AlphaGo” opinions covered AI companies, products and comprehension abilities.[Limitations] We only examined the proposed model with the topic of AlphaGo.[Conclusions] The proposed method benefits intelligence analysis.

Key wordsOpinion Mining      Sentiment Analysis      Confrontation Analysis     
Received: 03 December 2018      Published: 25 July 2020
ZTFLH:  TP391  
Corresponding Authors: Yu Qianqian     E-mail:

Cite this article:

Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis. Data Analysis and Knowledge Discovery, 2020, 4(7): 110-117.

URL:     OR

Technical Process
Interactive Data Network
数据ID 回答ID 问题ID 作者ID 作者名 点赞数 文本
1 104384323 41191131 3e9800f109ea6110a47c1a62aa9a7544 bai-xiao-tian-10-28 20 ……柯洁,18岁三冠,现役世界第一。阿法狗,最强围棋人工智能,4-1战胜李世乭……
2 104753993 41191131 078611a0e7dda6409f09bb8022a0d2f0 Dtcj 23 ……阿法狗打算靠什么下赢柯洁?……传统的对弈机器人是根据“一步棋子带来的所有的可能性”来布局的……
300 174316878 60279568 5503adb1d7ff5d58f0a76c80cce0e9c4 liang-yi-cong 756 比柯洁逼出更好的alphago更令人惊喜的是,alphago逼出了更好的柯洁……
4758 90009325 41171543 51fbc88fa0fa89f561c02d168f936601 liao-feng-95 0 我不认为阿尔法是真正意义上的人工智能。它只是一台按照既定规则做事并且计算能力惊人的电脑……
Zhihu Interactive Data (Partial)
Topic Distribution Visualization on Positive Text
Topic Distribution Visualization on Negative Text
倾向分类 主题一 主题二 主题三
积极倾向 AlphaGo、人类、机器、人工智能、围棋、柯洁、李世石、master、棋手、战胜、算法、学习、期待、科技、强大 人工智能、AlphaGo、人类、战胜、机器、围棋、输、李世石、柯洁、算法、理解、智慧、关注、见证、时代 AlphaGo、人类、围棋、人工智能、李世石、master、战胜、机器、棋手、柯洁、比赛、工具、超越、未来
消极倾向 理解、机器、AlphaGo、人类、围棋、李世石、人工智能、计算、棋手、经验、规则、局限性、打劫、判断、自我意识 输、人类、人工智能、李世石、AlphaGo、机器、战胜、围棋、理解、能力、谷歌、可怕、消灭、未来、统治 理解、AlphaGo、人类、机器、围棋、战胜、李世石、人工智能、算法、直觉、能力、大局观、逻辑、思维、人脑
Adversarial Opinion
[1] Jurczyk P, Agichtein E. Discovering Authorities in Question Answer Communities by Using Link Analysis[C] //Proceedings of the 16th ACM Conference on Information and Knowledge Management. 2007: 919-922.
[2] John B M, Chua A Y K, Goh D H L. What Makes a High-quality User-generated Answer?[J]. IEEE Internet Computing, 2011,15(1):66-71.
[3] Fu H Y, Wu S H, Oh S H. Evaluating Answer Quality Across Knowledge Domains: Using Textual and Non-textual Features in Social Q&A[C] // Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community. 2015: Article No. 88.
[4] Agichtein E, Castillo C, Donato D, et al. Finding High-quality Content in Social Media[C] // Proceedings of the 2008 International Conference on Web Search and Data Mining. 2008: 183-194.
[5] Hatzivassiloglou V, McKeown K R. Predicting the Semantic Orientation of Adjectives[C] //Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics. 1997: 174-181.
[6] 程亚男, 王宇. 基于语义情感相似度的问答社区答案排序研究[J]. 情报科学, 2018,36(8):72-76,83.
[6] ( Cheng Ya’nan, Wang Yu. Research on Ranking Q&A Community Answers Based on Semantic Emotional and Similarity[J]. Information Science, 2018,36(8):72-76, 83.)
[7] 姜雯, 许鑫, 武高峰. 附加情感特征的在线问答社区信息质量自动化评价[J]. 图书情报工作, 2015,59(4):100-105.
[7] ( Jiang Wen, Xu Xin, Wu Gaofeng. Online Q&A Community Automatically Information Quality Evaluation with Sentiment Feature[J]. Library and Information Service, 2015,59(4):100-105.)
[8] 刘渊杰. 社区问答系统最佳回答机制的研究[D]. 上海:上海交通大学, 2010.
[8] ( Liu Yuanjie. Study on Best Answer Policies in Community-based Question Answering Services[D]. Shanghai: Shanghai JiaoTong University, 2010.)
[9] 邹杰. 面向编程问答网站的主题挖掘及其应用研究[D]. 重庆:重庆大学, 2017.
[9] ( Zou Jie. Research on Topic Mining on Programming Question Answering Sites and Its Application[D]. Chongqing: Chongqing University, 2017.)
[10] 战学刚, 王晓. 基于LDA的问答网站话题抽取算法[J]. 计算机应用与软件, 2016,33(4):95-98.
[10] ( Zhan Xuegang, Wang Xiao. LDA-based Q&A Websites Question Label Extraction Algorithm[J]. Computer Applications and Software, 2016,33(4):95-98.)
[11] 倪兴良. 问答系统中的短文本聚类研究与应用[D]. 合肥:中国科学技术大学, 2011.
[11] ( Ni Xingliang. Short Text Clustering Research and Application in Q&A System[D]. Hefei: University of Science and Technology of China, 2011.)
[12] Madria S K, Bhowmick S S, Ng W K, et al. Research Issues in Web Data Mining[C] // Proceedings of the 1st International Conference on Data Warehousing and Knowledge Discovery. 1999: 303-312.
[13] Ortigosa-Hernández J, Rodríguez J D, Alzate L, et al. Approaching Sentiment Analysis by Using Semi-supervised Learning of Multi-dimensional Classifiers[J]. Neurocomputing, 2012,92(3):98-115.
[14] Socher R, Pennington J, Huang E H, et al. Semi-supervised Recursive Autoencoders for Predicting Sentiment Distributions[C] //Proceedings of the 8th Conference on Empirical Methods in Natural Language Processing. 2011: 151-161.
[1] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[2] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[3] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[4] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[5] Li Feifei,Wu Fan,Wang Zhongqing. Sentiment Analysis with Reviewer Types and Generative Adversarial Network[J]. 数据分析与知识发现, 2021, 5(4): 72-79.
[6] Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method[J]. 数据分析与知识发现, 2021, 5(3): 121-131.
[7] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[8] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[9] Zheng Xinman, Dong Yu. Constructing Degree Lexicon for STI Policy Texts[J]. 数据分析与知识发现, 2021, 5(10): 81-93.
[10] Hua Bin, Wu Nuo, He Xin. Integrating Expert Reviews for Government Information Projects with Knowledge Fusion[J]. 数据分析与知识发现, 2021, 5(10): 124-136.
[11] Lv Huakui,Liu Zhenghao,Qian Yuxing,Hong Xudong. Relationship Between Financial News and Stock Market Fluctuations[J]. 数据分析与知识发现, 2021, 5(1): 99-111.
[12] Jiang Lin,Zhang Qilin. Research on Academic Evaluation Based on Fine-Grain Citation Sentimental Quantification[J]. 数据分析与知识发现, 2020, 4(6): 129-138.
[13] Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing[J]. 数据分析与知识发现, 2020, 4(5): 1-14.
[14] Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
[15] Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938