Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (4): 64-71    DOI: 10.11925/infotech.1003-3513.2016.04.08
Orginal Article Current Issue | Archive | Adv Search |
Sentiment Analysis of Financial Forum Textual Message
Lan Qiujun(),Liu Wenxing,Li Weikang,Hu Xingye
Business School, Hunan University, Changsha 410082, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper aims to identify sentiment propensity accurately with the help of a new method based on dependency parsing. [Methods] First, we extracted the sentiment stems of the sentences. Second, we defined sentiment-computing rules. Finally, we calculated sentiment propensity of each sentence. [Results] The proposed method achieved an overall accuracy of 84.46%. The average precision rate and recall rate for bullish class were 82.84% and 87.14% respectively, with an F-measure of 84.94%. In the mean time, bearish class got a precision rate of 86.28%, a recall rate of 81.74% and an F-measure of 83.95%. [Limitations] The proposed method did not consider the relevance among clauses. [Conclusions] The dependency parsing can effectively improve the accuracy of sentiment analysis of textual message from financial forum.

Key wordsSentiment analysis      Dependency parsing      Financial forum text      Text mining     
Received: 14 October 2015      Published: 13 May 2016

Cite this article:

Lan Qiujun,Liu Wenxing,Li Weikang,Hu Xingye. Sentiment Analysis of Financial Forum Textual Message. New Technology of Library and Information Service, 2016, 32(4): 64-71.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.04.08     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I4/64

[1] Liu B.Sentiment Analysis and Opinion Mining[M]. California: Morgan and Claypool Publishers, 2012.
[2] Smailovi? J, Gr?ar M, Lavra? N, et al.Stream-based Active Learning for Sentiment Analysis in the Financial Domain[J]. Information Sciences, 2014, 285: 181-203.
[3] Van de Kauter M, Breesch D, Hoste V. Fine-grained Analysis of Explicit and Implicit Sentiment in Financial News Articles[J]. Expert Systems with Applications, 2015, 42(11): 4999-5010.
[4] Hagenau M, Liebmann M, Neumann D.Automated News Reading: Stock Price Prediction Based on Financial News Using Context-capturing Features[J]. Decision Support Systems, 2013, 55(3): 685-697.
[5] 胡勇军, 江嘉欣, 常会友. 基于LDA高频词扩展的中文短文本分类[J]. 现代图书情报技术, 2013(6): 42-48.
[5] (Hu Yongjun, Jiang Jiaxin, Chang Huiyou.A New Method of Keywords Extraction for Chinese Short-text Classification[J]. New Technology of Library and Information Service, 2013(6): 42-48.)
[6] Kiritchenko S, Zhu X, Mohammad S M.Sentiment Analysis of Short Informal Texts[J]. Journal of Artificial Intelligence Research, 2014, 50: 723-762.
[7] 段江娇, 刘红忠, 曾剑平. 投资者情绪指数、分析师推荐指数与股指收益率的影响研究——基于我国东方财富网股吧论坛、新浪网分析师个股评级数据[J]. 上海金融, 2014(11): 60-64.
[7] (Duan Jiangjiao, Liu Hongzhong, Zeng Jianping.A Study of the Influence Between Investor Sentiment Index, Analyst Recommendation Index and Stock Index Return —— Based on Eastmoney.com and Sina Analyst Shares Rating Data[J]. Shanghai Finance, 2014 (11): 60-64.)
[8] 林炳灿. 基于投资者情绪的网络舆论对股票价格影响的统计研究[D]. 成都: 西南财经大学, 2013.
[8] (Lin Bingcan.Statistical Studies of Network Public Opinion’s Investor Sentiment’s Impact on the Stock Price[D]. Chengdu: Southwestern University of Finance and Economics, 2013.)
[9] 陈江鹏. 基于网络舆论的我国股票市场有效性检验研究[D]. 成都: 西南财经大学, 2013.
[9] (Chen Jiangpeng.A Study of China’s Stock Market Effectiveness Testing Based on the Network Public Opinion [D]. Chengdu: Southwestern University of Finance and Economics, 2013.)
[10] 刘定平. 突发事件环境下投资者情绪对股票价格波动影响的实证研究[D]. 成都: 西南财经大学, 2014.
[10] (Liu Dingping.Empirical Research on the Volatility of Stock Price Brought by Investor’s Sentiment in the Emergency Environment[D]. Chengdu: Southwestern University of Finance and Economics, 2014.)
[11] 张世军, 程国胜, 蔡吉花, 等. 基于网络舆情支持向量机的股票价格预测研究[J]. 数学的实践与认识, 2013, 43(24): 33-40.
[11] (Zhang Shijun, Cheng Guosheng, Cai Jihua, et al.Stock Price Prediction Base on Network Public Opinion and Support Vector Machine[J]. Mathematics in Practice and Theory, 2013, 43(24): 33-40.)
[12] 宋敏晶. 基于情感分析的股票预测模型研究[D]. 哈尔滨: 哈尔滨工业大学, 2013.
[12] (Song Minjing.Stock Prediction Model Based on Sentiment Analysis Research[D]. Harbin: Harbin Institute of Technology, 2013.)
[13] 金雪军, 祝宇, 杨晓兰. 网络媒体对股票市场的影响——以东方财富网股吧为例的实证研究[J]. 新闻与传播研究, 2013(12): 36-51.
[13] (Jin Xuejun, Zhu Yu, Yang Xiaolan.Effects of Online Media on Stock Market: An Empirical Study on Eastmoney.com[J]. Journalism & Communication, 2013(12): 36-51.)
[14] 沈翰彬. 投资者本地关注对股票收益率的影响——基于网络论坛文本挖掘的实证研究[D]. 杭州: 浙江大学, 2014.
[14] (Shen Hanbin.The Effect of Investor Home Attention on Stock Return - A Study Based on Stock Message Boards with Text Mining Techniques [D]. Hangzhou: Zhejiang University, 2014.)
[15] 夏梦南, 杜永萍, 左本欣. 基于依存分析与特征组合的微博情感分析[J]. 山东大学学报: 理学版, 2014, 49(11): 22-30.
[15] (Xia Mengnan, Du Yongping, Zuo Benxin.Micro-blog Opinion Analysis Based on Syntactic Dependency and Feature Combination[J]. Journal of Shandong University: Natural Science, 2014, 49(11): 22-30.)
[16] 张庆庆, 刘西林. 基于依存句法关系的文本情感分类研究[J]. 计算机工程与应用, 2015, 51(22): 28-32.
[16] (Zhang Qingqing, Liu Xilin.Sentiment Analysis Based on Dependency Syntactic Relation[J]. Computer Engineering and Applications, 2015, 51(22): 28-32.)
[17] Nakagawa T, Inui K, Kurohashi S.Dependency Tree-based Sentiment Classification Using CRFs with Hidden Variables [C]. In: Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2010: 786-794.
[18] 肖红, 许少华. 基于句法分析和情感词典的网络舆情倾向性分析研究[J]. 小型微型计算机系统, 2014, 35(4): 811-813.
[18] (Xiao Hong, Xu Shaohua.Analysis on Web Public Opinion Orientation Based on Syntactic Parsing and Emotional Dictionary[J]. Journal of Chinese Computer Systems, 2014, 35(4): 811-813.)
[19] Turney P D, Littman M L.Measuring Praise and Criticism: Inference of Semantic Orientation from Association[J]. ACM Transactions on Information Systems, 2003, 21(4): 315-346.
[20] 姚天防, 娄德成. 汉语语句主题语义倾向分析方法的研究[J]. 中文信息学报, 2007, 21(5): 73-79.
[20] (Yao Tianfang, Lou Decheng.Research on Semantic Orientation Analysis for Topics in Chinese Sentences[J]. Journal of Chinese Information Processing, 2007, 21(5): 73-79.)
[21] Che W X, Li Z H, Liu T.LTP: A Chinese Language Technology Platform [C]. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Beijing, China.2010: 13-16.
[22] 刘挺, 马金山. 汉语自动句法分析的理论与方法[J]. 当代语言学, 2009, 11(2): 100-112.
[22] (Liu Ting, Ma Jinshan.Theories and Methods of Chinese Automatic Syntactic Parsing: A Critical Survey[J]. Contemporary Linguistics, 2009, 11(2): 100-112.)
[23] 万常选, 江腾蛟, 钟敏娟, 等. 基于词性标注和依存句法的Web金融信息情感计算[J]. 计算机研究与发展, 2013, 50(12): 2554-2569.
[23] (Wan Changxuan, Jiang Tengjiao, Zhong Minjuan, et al.Sentiment Computing of Web Financial Information Based on the Part-of-Speech Tagging and Dependency Parsing[J]. Journal of Computer Research and Development, 2013, 50(12): 2554-2569.)
[24] NLPIR/ICTCLAS 汉语分词系统[EB/OL]. [2013-07-02]. .
[24] (NLPIR/ICTCLAS Chinese Segmentation System [EB/OL]. [2013-07-02].
[25] Cui H, Mittal V, Datar M.Comparative Experiments on Sentiment Classification for Online Product Reviews[C]. In: Proceedings of the 21st National Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2006: 1265-1270.
[1] Fan Tao,Wang Hao,Wu Peng. Sentiment Analysis of Online Users' Negative Emotions Based on Graph Convolutional Network and Dependency Parsing[J]. 数据分析与知识发现, 2021, 5(9): 97-106.
[2] 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.
[3] Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[4] 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.
[5] Xu Guang,Ren Ming,Song Chengyu. Extracting China’s Economic Image from Western News[J]. 数据分析与知识发现, 2021, 5(5): 30-40.
[6] 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.
[7] 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.
[8] 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.
[9] Dai Bing,Hu Zhengyin. Review of Studies on Literature-Based Discovery[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] Yu Chuanming, Wang Manyi, Lin Hongjun, Zhu Xingyu, Huang Tingting, An Lu. A Comparative Study of Word Representation Models Based on Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 28-40.
[15] Xia Tian. Extracting Key-phrases from Chinese Scholarly Papers[J]. 数据分析与知识发现, 2020, 4(7): 76-86.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn