Emotion Classification Technology for Online Video Comments on the Internet

doi:10.3969/j.issn.1671-1122.2019.05.008

Abstract

Abstract:

With the rapid growth of online videos, more and more people begin to publish comments on videos of video websites. Users' comments usually include personal emotions and some of the key information about the videos, which makes significant impact on video viewing decisions for Web users. Emotion classification and extracting key words from online video comments automatically have become an urgent problem. This paper focuses on the emotion classification for online video comments, and analyzes the influence of different feature extraction and feature selection methods and different classification algorithms on the accuracy of online video comments emotion classification. Simulation results show that the online video comments emotion classification model proposed in this paper has high accuracy.

Key words: emotion classification, classification algorithm, feature extraction, feature selection

CLC Number:

TP309

Hui LI, Shice NI, Jia XIAO, Tianzhong ZHAO. Emotion Classification Technology for Online Video Comments on the Internet[J]. Netinfo Security, 2019, 19(5): 61-68.

Figures/Tables 12

References 18

[1]	YE Qiang, SHI Wen, LI Yijun.Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach[C]//HICSS. The 39th Annual Hawaii International Conference on System Sciences, January 4-7, 2006, Kauia, HI, USA. NJ: IEEE, 2006: 53b.
[2]	SHENG Chengcheng, ZHU Yong, LIU Tao.Public Opinion Analysis Based on Weibo Socail Network[J]. Intelligent Computer and Applications, 2019, 9(1): 57-64.
	盛成成,朱勇,刘涛.基于微博社交平台的舆情分析[J]. 智能计算机与应用,2019,9(1):57-64.
[3]	DAVE K, LAWRENCE S, PENNOCK D M.Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews[C]// ACM. The 12th International Conference on World Wide Web, May 20 - 24, 2003, Budapest, Hungary. New York: ACM, 2003: 519-528.
[4]	CHEN Zhi, LI Peng.Text Sentiment Classification Based on Mutual Information and Cluster Sampling[J]. Modern Computer, 2015(4): 14-20.
	陈智,李鹏. 利用互信息和聚类抽样的文本情感分类[J]. 现代计算机:普及版,2015(4):14-20.
[5]	WAN Houbin.Research on Feature Dimension Reduction in Text Classification[D]. Chongqing: Chongqing University, 2012.
	万斌候. 文本分类中的特征降维方法研究[D]. 重庆:重庆大学,2012.
[6]	GAO Yuan.An Overview of the Development and Application of Natural Language Processing[J]. China New Telecommunications, 2019, 21(2): 117-118.
	高源. 自然语言处理发展与应用概述[J].中国新通信,2019,21(2):117-118.
[7]	LIU Zhixiang.Research and Implementation of Two Text Categorization Algorithms[D]. Changchun: Jilin University, 2008.
	刘智祥. 两种文本分类算法的研究与实现[D]. 长春:吉林大学,2008.
[8]	KIM J, CHOE D, KIM G, et al.Noise Removal Using TF-IDF Criterion for Extracting Patent Keyword[M]// Springer. Soft Computing in Big Data Processing. Cham: Springer, Cham, 2014: 1107-1127.
[9]	CUI Hang, MITTAL V, DATAR M.Comparative Experiments on Sentiment Classification for Online Product Reviews[C]//AAAI. The 21st National Conference on Artificial Iintelligence, July 16-20, 2006, Boston, Massachusetts, USA. Palo Alto: AAAI Press, 2006: 61-80.
[10]	YANG Liyue, WANG Yizhi.Research on Construction and Analysis of Emotion Dictionary in Emotion Analysis of Micro-blog[J]. Computer Technology and Development, 2019(2): 13-18.
	杨立月,王移芝. 微博情感分析的情感词典构造及分析方法研究[J]. 计算机技术与发展,2019(2):13-18.
[11]	CSDN.最全中文停用词表整理[EB/OL]. ,2014-9-30.
[12]	PANG Bo, LEE L, VAITHYANATHAN S.Thumbs up?: Sentiment Classification Using Machine Learning Techniques[C]//EMNLP. Conference on Empirical Methods in Natural Language Processing, July 6-7, 2002, Philadelphia, PA, USA. Association for Computational Linguistics, 2002: 79-86.
[13]	DAI Liuling, HUANG Heyan, CHEN Zhaoxiong.A Comparative Study on Feature Selection in Chinese Text Categorization[J]. Journal of Chinese Information Processing, 2004, 18(1): 26-32.
	代六玲,黄河燕,陈肇雄. 中文文本分类中特征抽取方法的比较研究[J]. 中文信息学报,2004,18(1):26-32.
[14]	LIU Zhiming, LIU Lu.Empirical Study of Sentiment Classification for Chinese Microblog Based on Machine Learning[J]. Computer Engineering and Applications, 2012, 48(1): 1-4.
	刘志明,刘鲁. 基于机器学习的中文微博情感分类实证研究[J]. 计算机工程与应用,2012,48(1):1-4.
[15]	ZHOU Jun, HE Li, HAN Weihong, et al.Research of Tendency Algorithm for Chinese Comments based on Machine Learning[J]. Netinfo Security, 2013, 13(10): 164-166.
	周军,何力,韩伟红,等.基于机器学习的中文评论倾向性分类实证研究[J].信息网络安全,2013,13(10):164-166.
[16]	DAI Wei.A Solution to Text Classification with Logical Regression[J]. Telecom World, 2018(8): 266-267.
	戴维. 逻辑回归解决文本分类问题[J]. 通讯世界,2018(8):266-267.
[17]	WANG Xinyu, ZHAO Mingtao, GUI Yang.Research on Face Recgnition Based on Classification Algorithms[J]. Journal of Jiaozuo University, 2019, 33(1): 56-61.
	王欣宇,赵明涛,桂扬. 基于分类算法的人脸识别研究[J]. 焦作大学学报,2019,33(1):56-61.
[18]	WAN Yueliang, ZHU Hejun, LIU Hongzhi.Research on Web Page Classification Method Based on Web Page Structural Tendency[J]. Netinfo Security, 2009, 9(9): 76-79.
	万月亮,朱贺军,刘宏志.基于网页结构化倾向的网页分类方法研究[J].信息网络安全,2009,9(9):76-79.

	文本实际属于某类	文本实际不属于某类
判定为某类	A	B
判定为非某类	C	D

过程	测试编号	耗费时间/s	平均时间/s
训练情感分类模型	测试1	874.56	879.47
	测试2	885.49
	测试3	878.35
对评论进行情感分类	测试1	2876.56	2941.71
	测试2	2992.27
	测试3	2956.29