Sentiment analysis method combining sentiment and semantic information

doi:10.11772/j.issn.1001-9081.2018112375

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 1931-1935.DOI: 10.11772/j.issn.1001-9081.2018112375

• Artificial intelligence • Previous Articles Next Articles

Sentiment analysis method combining sentiment and semantic information

MENG Shilin¹, ZHAO Yunlong^1,2, GUAN Donghai^1,2, ZHAI Xiangping^1,2

1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 211106, China;
2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing Jiangsu 210023, China

Received:2018-12-03 Revised:2019-02-17 Online:2019-07-15 Published:2019-07-10
Supported by:
This work is partially supported by the National Defense Basic Scientific Research Program (JCKY2016605B006), the Six Talent Peaks Project of Jiangsu Province (XYDXXJS-031), the Fundamental Research Funds for the Central Universities (90YAH16042).

融合情感与语义信息的情感分析方法

孟仕林¹, 赵蕴龙^1,2, 关东海^1,2, 翟象平^1,2

1. 南京航空航天大学计算机科学与技术学院, 南京 211106;
2. 软件新技术与产业化协同创新中心, 南京 210023

通讯作者: 赵蕴龙
作者简介:孟仕林(1994-),男,安徽界首人,硕士研究生,CCF会员,主要研究方向:数据挖掘、情感分析;赵蕴龙(1975-),男,黑龙江哈尔滨人,教授,博士,CCF会员,主要研究方向:无线网络、群体计算、说话人识别、数据挖掘、可穿戴计算技术;关东海(1981-),男,黑龙江哈尔滨人,副教授,博士,CCF会员,主要研究方向:语音识别、数据挖掘、人工智能;翟象平(1984-),男,山东淄博人,副教授,博士,CCF会员,主要研究方向:普适计算、蜂群无人系统、群体智能、数据挖掘。
基金资助:
国防基础科研计划资助项目（JCKY2016605B006）；江苏省"六大人才高峰"高层次人才项目（XYDXXJS-031）；中央高校基本科研业务费专项（90YAH16042）。

Abstract

Abstract:

When using word embedding method for word-to-vector, two antonyms are converted into similar vectors. If they are sentiment words, it will lead to the loss of sentimental information, which is unreasonable in sentiment analysis task. To solve this problem, a method of adding sentiment vectors to obtain sentiment information based on word embedding was proposed. Firstly, the sentiment vector was constructed by using sentiment lexicon, and combined with word vector obtained by word embedding method. Then, a bidirectional Long Short Term Memory (BiLSTM) network was used to obtain the characteristics of text. Finally, the sentiment of text was classified. Experiments of the proposed method and the method without fusing sentimental vector were carried out on four datasets. The experimental results show that the classification accuracy and F1 score of the proposed method are higher than those of the method without fusion, which indicates that adding sentimental vectors is beneficial to improve the performance of sentiment analysis.

Key words: sentiment analysis, word embedding, sentiment word, sentiment information, bidirectional LSTM (BiLSTM)

摘要：

在使用词嵌入法进行词转向量时，两个反义词会转换成相近的向量。如果这两个词是情感词，将会导致词的情感信息的丢失，这在情感分析任务中是不合理的。为了解决这个问题，提出了一种在词嵌入的基础上增加情感向量来获取情感信息的方法。首先利用情感词典资源构建情感向量，将其与词嵌入法得到的词向量融合在一起；然后采用双向长短期记忆（BiLSTM）网络获取文本的特征；最后对文本的情感进行分类。在4个数据集上分别对该方法与未融合情感向量的方法进行了实验。实验结果表明所提方法分类准确度与F1值都高于未融合方法，说明了加入情感向量有助于提高情感分析的性能。

关键词: 情感分析, 词嵌入, 情感词, 情感信息, 双向长短期记忆网络

CLC Number:

TP389.1

MENG Shilin, ZHAO Yunlong, GUAN Donghai, ZHAI Xiangping. Sentiment analysis method combining sentiment and semantic information[J]. Journal of Computer Applications, 2019, 39(7): 1931-1935.

孟仕林, 赵蕴龙, 关东海, 翟象平. 融合情感与语义信息的情感分析方法[J]. 计算机应用, 2019, 39(7): 1931-1935.

References

[1] 吴鹏,应杨,沈思.基于双向长短期记忆模型的网民负面情感分类研究[J].情报学报,2018,37(08):845-853.(WU P, YING Y, SHEN S. Negative emotions of online users' analysis based on bidirectional long short-term memory[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(8):845-853.)
[2] 赵刚,徐赞.基于机器学习的商品评论情感分析模型研究[J].信息安全研究,2017,3(2):166-170.(ZHAO G, XU Z. Research on the sentiment analysis model of product reviews based on machine learning[J]. Journal of Information Security Research, 2017, 3(2):166-170.)
[3] TETSUYA N, JEONGHEE Y. Sentiment analysis:Capturing favorability using natural language processing[C]//Proceedings of the 2nd International Conference on Knowledge Capture. New York:ACM, 2003:70-77.
[4] WEI J, HUNG H H, ROHINI K S. OpinionMiner:a novel machine learning system for Web opinion mining and extraction[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2009:1195-1204.
[5] ZOU H, TANG X, XIE B, et al. Sentiment classification using machine learning techniques with syntax features[C]//Proceedings of the 2015 International Conference on Computational Science and Computational Intelligence. Piscataway, NJ:IEEE, 2015:175-179.
[6] YIN R, LI P, WANG B. Sentiment lexical-augmented convolutional neural networks for sentiment analysis[C]//Proceedings of the 2017 IEEE 2nd International Conference on Data Science in Cyberspace. Piscataway, NJ:IEEE, 2017:630-635.
[7] 於雯,周武能.基于LSTM的商品评论情感分析[J].计算机系统应用,2018,27(8):159-163.(YU W, ZHOU W N. Sentiment analysis of commodity reviews based on LSTM[J]. Computer Systems and Applications, 2018, 27(8):159-163.)
[8] 丁晟春,吴靓婵媛,李红梅.基于SVM的中文微博观点倾向性识别[J].情报学报,2016,35(12):1235-1243.(DING S C, WU J C Y, LI H M. SVM-based Chinese microblogging viewpoint orientation recognition[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(12):1235-1243.)
[9] BENGIO Y, DUCHARME R, VINCENT P, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3:1137-1155.
[10] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[J]. Advances in Neural Information Processing Systems, 2013, 26:3111-3119.
[11] 何炎祥,孙松涛,牛菲菲,等.用于微博情感分析的一种情感语义增强的深度学习模型[J].计算机学报,2017,40(4):773-790.(HE Y X, SUN S T, NIU F F, et al. An emotional semantic enhanced deep learning model for microblog emotion analysis[J]. Chinese Journal of Computers, 2017, 40(4):773-790.)
[12] YANG Z, YANG D, DYER C, et al. Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2016:1480-1489.
[13] WANG Y Q, HUANG M L, ZHAO L, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2016:606-615.
[14] ALEC Y, ABHISHEK. V. Deep CNN-LSTM with combined kernels from multiple branches for IMDB review sentiment analysis[C]//Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference. Piscataway, NJ:IEEE, 2017:540-546.
[15] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[C]//Proceedings of the 2013 International Conference on Learning Representations. Scottsdale, Arizona:[s.n.], 2013:1-12.
[16] 谭松波.酒店评论数据[EB/OL].[2018-10-20]. http://www.datatang.com/data/11970.(TAN S B. Hotel review data[EB/OL].[2018-10-20]. http://www.datatang.com/data/11970.)

Sentiment analysis method combining sentiment and semantic information

融合情感与语义信息的情感分析方法

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