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| Exploration of Intellectual Structure and Hot Issues in Sentiment Analysis Research |
| Zhou Jian, Liu Yanbao, Liu Jiajia |
| School of Management, Shanghai University, Shanghai 200444 |
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Abstract To comprehensively and systematically understand the development of affective analysis, this paper conducts bibliometric analysis and knowledge map analysis of affective analysis-themed literature included in the Web of Science with the help of software such as CiteSpace, UCINET, BICOMB, and SPSS. Using co-citation analysis, cluster analysis, co-word analysis, strategic coordinate analysis, and other methods, the knowledge structure, research foci, and trends in this field are revealed as follows: (i) The application of affective analysis, deep learning, and neural networks, product reviews of e-commerce, marking of emotional characteristics of things, user-generated content under social networks, semantic-oriented advertising technology, and textual language attribute analysis constitutes the knowledge structure of affective analysis. (ii) The researchers in this field focus on product comments and reputation, data mining and artificial intelligence, unsupervised learning, Hadoop-MapReduce and support vector machines, and neural networks and deep learning. (iii) Future research mainly includes customer reviews, recommendation systems, polarity classifications, subject models, movie reviews, and Twitter data.
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Received: 13 March 2019
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1 Pang B,Lee L,Vaithyanathan S. Thumbs up?: Sentiment classification using machine learning techniques[C]// Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2002, 10: 79-86.
2 Turney P D. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews[C]// Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2002: 417-424.
3 Archak N,Ghose A,Ipeirotis P G. Deriving the pricing power of product features by mining consumer reviews[J]. Management Science, 2011, 57(8): 1485-1509.
4 郑丽娟, 王洪伟, 郭恺强. 中文网络评论的情感分类: 句子与段落的比较研究[J]. 情报学报, 2013, 32(4): 376-384.
5 Hu M Q,Liu B. Mining opinion features in customer reviews[C]// Proceedings of the 19th National Conference on Artifical Intelligence. Palo Alto: AAAI Press, 2004: 755-760.
6 Dave K,Lawrence S,Pennock D M. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews[C]// Proceedings of the 12th International Conference on World Wide Web. New York: ACM Press, 2003: 519-528.
7 Li N,Wu D D. Using text mining and sentiment analysis for online forums hotspot detection and forecast[J]. Decision Support Systems, 2010, 48(2): 354-368.
8 Paltoglou G,Giachanou A. Opinion retrieval: Searching for opinions in social media[M]// Professional Search in the Modern World. Cham: Springer, 2014: 193-214.
9 Renaud A,Walsh I,Kalika M. Is SAM still alive? A bibliometric and interpretive mapping of the strategic alignment research field[J]. The Journal of Strategic Information Systems, 2016, 25(2): 75-103.
10 杨书燕, 吴小节, 汪秀琼. 制度逻辑研究的文献计量分析[J]. 管理评论, 2017, 29(3): 90-109.
11 吴小节, 谭晓霞, 汪秀琼. 市场分割研究的知识结构与动态演化——基于1998—2015年CSSCI经济管理类期刊数据库的文献计量分析[J]. 管理评论, 2018, 30(12): 257-275.
12 唐晓波, 刘广超. 细粒度情感分析研究综述[J]. 图书情报工作, 2017, 61(5): 132-140.
13 王洪伟, 刘勰, 尹裴, 等. Web文本情感分类研究综述[J]. 情报学报, 2010, 29(5): 931-938.
14 赵蓉英, 张扬. 基于时空维度的国内外情感分析研究演化分析[J]. 情报科学, 2018, 36(10): 171-177.
15 Chen C M. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377.
16 Law J,Bauin S,Courtial J P, et al. Policy and the mapping of scientific change: A co-word analysis of research into environmental acidification[J]. Scientometrics, 1988, 14(3-4): 251-264.
17 Kim M C,Chen C M. A scientometric review of emerging trends and new developments in recommendation systems[J]. Scientometrics, 2015, 104(1): 239-263.
18 Taboada M,Brooke J,Tofiloski M, et al. Lexicon-based methods for sentiment analysis[J]. Computational Linguistics, 2011, 37(2): 267-307.
19 Bollen J,Mao H N,Zeng X J. Twitter mood predicts the stock market[J]. Journal of Computational Science, 2011, 2(1): 1-8.
20 Thelwall M,Buckley K,Paltoglou G, et al. Sentiment strength detection in short informal text[J]. Journal of the American Society for Information Science and Technology, 2010, 61(12): 2544-2558.
21 Medhat W,Hassan A,Korashy H. Sentiment analysis algorithms and applications: A survey[J]. Ain Shams Engineering Journal, 2014, 5(4): 1093-1113.
22 Cambria E. Affective computing and sentiment analysis[J]. IEEE Intelligent Systems, 2016, 31(2): 102-107.
23 Maas A L,Daly R E,Pham P T, et al. Learning word vectors for sentiment analysis[C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2011, 1: 142-150.
24 Socher R,Perelygin A,Wu J Y, et al. Recursive deep models for semantic compositionality over a sentiment treebank[C]// Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2013: 1631-1642.
25 Poria S,Cambria E,Gelbukh A. Aspect extraction for opinion mining with a deep convolutional neural network[J]. Knowledge-Based Systems, 2016, 108: 42-49.
26 Pennington J,Socher R,Manning C. Glove: Global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1532-1543.
27 Pang B,Lee L. Opinion mining and sentiment analysis[J]. Foundations and Trends? in Information Retrieval, 2008, 2(1-2): 1-135.
28 Liu B. Sentiment analysis and opinion mining[J]. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1-167.
29 Tumasjan A,Sprenger T O,Sandner P G, et al. Predicting elections with Twitter: What 140 characters reveal about political sentiment[C]// Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2010: 178-185.
30 Hall M,Frank E,Holmes G, et al. The WEKA data mining software[J]. ACM SIGKDD Explorations Newsletter, 2009, 11(1): 10-18.
31 Liu B,Hu M Q,Cheng J S. Opinion observer: Analyzing and comparing opinions on the Web[C]// Proceedings of the 14th International Conference on World Wide Web. New York: ACM Press, 2005: 342-351.
32 Kim S M,Hovy E. Determining the sentiment of opinions[C]// Proceedings of the 20th International Conference on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2004: 1367.
33 Liu B. Sentiment analysis and subjectivity[M]// Indurkhya N, Damerau F J. Handbook of Natural Language Processing, Second Edition. 2010, 2: 627-666.
34 Ding X W,Liu B,Yu P S. A holistic lexicon-based approach to opinion mining[C]// Proceedings of the 2008 International Conference on Web Search and Data Mining. New York: ACM Press, 2008: 231-240.
35 Hu M Q,Liu B. Mining and summarizing customer reviews[C]// Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2004: 168-177.
36 Jo Y,Oh A H. Aspect and sentiment unification model for online review analysis[C]// Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2011: 815-824.
37 Manning C D,Raghavan P,Schütze H. Text classification and naive Bayes[M]// Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008: 234-265.
38 Wilson T,Wiebe J,Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis[C]// Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2005: 347-354.
39 Das S R,Chen M Y. Yahoo! for Amazon: Sentiment extraction from small talk on the Web[J]. Management Science, 2007, 53(9): 1375-1388.
40 Esuli A,Sebastiani F. Determining term subjectivity and term orientation for opinion mining[C]// Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2006: 193-200.
41 Thet T T,Na J C,Khoo C S G. Aspect-based sentiment analysis of movie reviews on discussion boards[J]. Journal of Information Science, 2010, 36(6): 823-848.
42 Cambria E,Schuller B,Xia Y Q, et al. New avenues in opinion mining and sentiment analysis[J]. IEEE Intelligent Systems, 2013, 28(2): 15-21.
43 Thelwall M,Buckley K,Paltoglou G. Sentiment strength detection for the social web[J]. Journal of the American Society for Information Science and Technology, 2012, 63(1): 163-173.
44 Feldman R. Techniques and applications for sentiment analysis[J]. Communications of the ACM, 2013, 56(4): 82-89.
45 Xia R,Zong C Q,Li S S. Ensemble of feature sets and classification algorithms for sentiment classification[J]. Information Sciences, 2011, 181(6): 1138-1152.
46 Abbasi A,Chen H C,Salem A. Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums[J]. ACM Transactions on Information Systems, 2008, 26(3): Article No. 12.
47 Lin C H,He Y L. Joint sentiment/topic model for sentiment analysis[C]// Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York: ACM Press, 2009: 375-384.
48 Ravi K,Ravi V. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications[J]. Knowledge-Based Systems, 2015, 89: 14-46.
49 Mohammad S M,Turney P D. Crowdsourcing a word-emotion association lexicon[J]. Computational Intelligence, 2013, 29(3): 436-465.
50 Kiritchenko S,Zhu X,Mohammad S M. Sentiment analysis of short informal texts[J]. Journal of Artificial Intelligence Research, 2014, 50: 723-762.
51 Popescu A M,Nguyen B,Etzioni O. OPINE: Extracting product features and opinions from reviews[C]// Proceedings of HLT/EMNLP on Interactive Demonstrations. Stroudsburg: Association for Computational Linguistics, 2005: 32-33.
52 Godbole N,Srinivasaiah M,Skiena S. Large-scale sentiment analysis for news and blogs[C]// Proceedings of the International Conference on Weblogs and Social Media, 2007: 219-222.
53 Saif H,He Y L,Fernandez M, et al. Contextual semantics for sentiment analysis of Twitter[J]. Information Processing & Management, 2016, 52(1): 5-19.
54 Giatsoglou M,Vozalis M G,Diamantaras K, et al. Sentiment analysis leveraging emotions and word embeddings[J]. Expert Systems with Applications, 2017, 69: 214-224.
55 Salehan M,Kim D J. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics[J]. Decision Support Systems, 2016, 81: 30-40.
56 DiCaro L,GrellaM. Sentiment analysis via dependency parsing[J]. Computer Standards & Interfaces, 2013, 35(5): 442-453.
57 Lin Z J. An empirical investigation of user and system recommendations in e-commerce[J]. Decision Support Systems, 2014, 68: 111-124.
58 Kumar N,Benbasat I. Research note: The influence of recommendations and consumer reviews on evaluations of websites[J]. Information Systems Research, 2006, 17(4): 425-439.
59 Goh K Y,Heng C S,Lin Z J. Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content[J]. Information Systems Research, 2013, 24(1): 88-107.
60 Jiang H M,Kwong C K,Yung K L. Predicting future importance of product features based on online customer reviews[J]. Journal of Mechanical Design, 2017, 139(11): 111413.
61 Aguwa C,Olya M H,Monplaisir L. Modeling of fuzzy-based voice of customer for business decision analytics[J]. Knowledge-Based Systems, 2017, 125: 136-145.
62 Kutschenreiter-Praszkiewicz I. Application of neural network in QFD matrix[J]. Journal of Intelligent Manufacturing, 2013, 24(2): 397-404.
63 Bloom K,Argamon S. Automated learning of appraisal extraction patterns[M]// Corpus-Linguistic Applications: Current Studies, New Directions. Brill Rodopi, 2010: 249-260.
64 Guo J L,Peng J E,Wang H C. An opinion feature extraction approach based on a multidimensional sentence analysis model[J]. Cybernetics and Systems, 2013, 44(5): 379-401.
65 Thakur R K,Deshpande M V. Kernel Optimized-Support Vector Machine and MapReduce framework for sentiment classification of train reviews[J]. Sādhanā, 2019, 44: 6.
66 Khan F H,Qamar U,Bashir S. SWIMS: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis[J]. Knowledge-Based Systems, 2016, 100: 97-111.
67 Araque O,Corcuera-Platas I,Sánchez-Rada J F, et al. Enhancing deep learning sentiment analysis with ensemble techniques in social applications[J]. Expert Systems with Applications, 2017, 77: 236-246.
68 Mai L,Le B. Aspect-based sentiment analysis of vietnamese texts with deep learning[C]// Proceedings of the Asian Conference on Intelligent Information and Database Systems. Cham: Springer, 2018: 149-158.
69 Alharbi A S M,de Doncker E. Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information[J]. Cognitive Systems Research, 2019, 54: 50-61.
70 Wang W Y,Pan S J,Dahlmeier D, et al. Recursive neural conditional random fields for aspect-based sentiment analysis[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 616-626. |
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