A Survey of the Applications of Sentiment Analysis
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A Survey of the Applications of Sentiment Analysis

Authors: Pingping Lin, Xudong Luo

Abstract:

Natural language often conveys emotions of speakers. Therefore, sentiment analysis on what people say is prevalent in the field of natural language process and has great application value in many practical problems. Thus, to help people understand its application value, in this paper, we survey various applications of sentiment analysis, including the ones in online business and offline business as well as other types of its applications. In particular, we give some application examples in intelligent customer service systems in China. Besides, we compare the applications of sentiment analysis on Twitter, Weibo, Taobao and Facebook, and discuss some challenges. Finally, we point out the challenges faced in the applications of sentiment analysis and the work that is worth being studied in the future.

Keywords: Natural language processing, sentiment analysis, application, online comments.

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[1] M.M. Ag¨uero-Torales, M.J. Cobo, E. Herrera-Viedma, and A.G. L´opez-Herrera. A cloud-based tool for sentiment analysis in reviews about restaurants on tripadvisor. Procedia Computer Science, pages 392–399, 2019.
[2] Al-Amin, M.A. Islam, S. Halder, M.A. Uddin, and U.K. Acharjee. An efficient sentiment mining approach on social media networks. In Proceedings of the 2019 Emerging Technologies in Data Mining and Information Security, volume 814, pages 451–461, 2019.
[3] M. Al-Smadi, M. Al-Ayyoub, and Y. Jararweh. Enhancing aspect-based sentiment analysis of arabic hotels’ reviews using morphological, syntactic and semantic features. Information Processing & Management, 56(2):308–319, 2019.
[4] M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, and Y. Jararweh. Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of arabic hotels’ reviews. Journal of Computational Science, 27:386–393, 2018.
[5] M. Alam, F. Abid, G.-P. Cong, and Y.-R. Lv. Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications. Computer Communications, 154:129–137, 2020.
[6] S. Alashri, S. Kandala, V. Bajaj, R. Ravi, K. Smith, and K. Desouza. An analysis of sentiments on facebook during the 2016 U.S. presidential election. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 795–802, 2016.
[7] M. Azab, R. Mihalcea, and J. Abernethy. Analysing ratemyprofessors evaluations across institutions, disciplines, and cultures: The tell-tale signs of a good professor. In Proceedings of the 2016 International Conference on Social Informatics, volume 10046, pages 438–453, 2016.
[8] A. Bakharia. Towards cross-domain MOOC forum post classification. In Proceedings of the Third ACM Conference Learning, pages 253–256, 2016.
[9] J.D. Bodapati, N. Veeranjaneyulu, and S. Shaik. Sentiment analysis from movie reviews using LSTM. Ingenierie des Systemes d’Information, 24(1):125–129, 2019.
[10] J. Burstein, B. Beigman-Klebanov, M. Nitin, and A. Faulkner. Automated sentiment analysis for essay evaluation. International Journal of Innovative and Emerging Research in Engineering, 2013.
[11] E. Cambria. Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2):102–107, 2016.
[12] E. Cambria, S. Poria, A. Gelbukh, and M. Thelwall. Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6):7480, 2017.
[13] A. Carvalho, A. Levitt, S. Levitt, E. Khaddam, and J. Benamati. Off-the-shelf artificial intelligence technologies for sentiment and emotion analysis: A tutorial on using IBM natural language processing. Communications of the Association for Information Systems, 44(43):918–943, 2019.
[14] N. Chambers, Bowen V., and E. Genco. Identifying political sentiment between nation states with social media. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 65–75, 2015.
[15] C. Chen, X.-L. Zhang, S. Ju, C.-L. Fu, C.-Z. Tang, J. Zhou, and X.-L. Li. Antprophet: An intention mining system behind alipay’s intelligent customer service bot. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 6497–6499, 2019.
[16] M.-M. Cheng and X. Jin. What do Airbnb users care about? an analysis of online review comments. International Journal of Hospitality Management, 76(Part A):58–70, 2019.
[17] K.G. Coffman and A.M. Odlyzko. Internet Growth: Is There a “Moore’s Law” for Data Traffic?, pages 47–93. HandBook of Massive Data Sets, 2002.
[18] F. Colace, M. Santo, and L. Greco. Safe: A sentiment analysis framework for e-learning. International Journal of Emerging Technologies in Learning, 9(6):37–41, 2014.
[19] M.Y. Day and Y.-D. Lin. Deep learning for sentiment analysis on google play consumer review. In Proceedings of the 2017 IEEE International Conference on Information Reuse and Integration, pages 382–388, 2017.
[20] F.S. Dolianiti, D. Iakovakis, S.B. Dias, S. Hadjileontiadou, J.A. Diniz, and L. Hadjileontiadis. Sentiment analysis techniques and applications in education: A survey. In Proceedings of the 2018 International Conference on Technology and Innovation in Learning, Teaching and Education, volume 993, pages 412–427, 2018.
[21] D. Gan, J. Shen, and M. Xu. Adaptive learning emotion identification method of short texts for online medical knowledge sharing community. Computational Intelligence and Neuroscience, 2019:1–10, 2019.
[22] Y. Gao and S. Wang. Application & comparison of several sentiment analysis platforms with open source review dataset. In Proceedings of the 3rd International Conference on Computer Science and Application Engineering, volume 78, pages 1–78, 2019.
[23] X. Guo and J.-H. Zhu. Deep neural network recommendation model based on user vectorization representation and attention mechanism. Computer Science, 46(8):111–115, 2019.
[24] S.W. Handani, D.I.S. Saputra, Hasirun, R.M. Arino, and G.F.A. Ramadhan. Sentiment analysis for Go-Jek on Google play store. Journal of Physics: Conference Series, 1196:12–32, 2019.
[25] D. Hazarika, S. Poria, R. Mihalcea, E. Cambria, and R. Zimmermann. Icon: Interactive conversational memory network for multimodal emotion detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, page 25942604, 2018.
[26] D. Hazarika, S. Poria, A. Zadeh, E. Cambria, L.-P. Morency, and R. Zimmermann. Conversational memory network for emotion recognition in dyadic dialogue videos. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1, page 21222132, 2018.
[27] B. Helversen, K. Abramczuk, W. Kopec, and R. Nielek. Influence of consumer reviews on online purchasing decisions in older and younger adults. Decision Support Systems, 113:1–10, 2018.
[28] Y.-Y. Hu, C.-B. Deng, and Z. Zhou. A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environments. Annals of the American Association of Geographers, 109(4):1052–1073, 2019.
[29] M. Hur, P. Kang, and S. Choc. Box-office forecasting based on sentiments of movie reviews and independent subspace method. Information Sciences, 372(1):608–624, 2016.
[30] J. Jabbar, I. Urooj, J.-S.Wu, and N. Azeem. Real-time sentiment analysis on e-commerce application. In Proceedings of the 2019 International Conference on Network Security, pages 391–396, 2019.
[31] J. James, L. Tian, and C. Watson. An open source emotional speechcorpus for human robot interaction applications. In Proceedings of 2018 Interspeech, pages 2768–2772, 2018.
[32] D. Janssen, C. Tummel, S. Jeschke, and A. Richert. Sentiment analysis of social media for evaluating universities. In Proceedings of Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications, pages 49–62, 2015.
[33] E. Kauffmann, D. Gil, J. Peral, A. Ferrandez, and R. Sellers. A step further in sentiment analysis application in marketing decision-making. In Proceedings of the 2019 International Research & Innovation Forum, pages 211–221, 2019.
[34] Q.-Q. Kong, C.-S. Yu, T. Iqbal, Y. Xu, W.-W. Wang, and M.D. Plumbley. Weakly labelled AudioSet tagging with attention neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(11):1791–1802, 2019.
[35] B. Liu. Sentiment Analysis and Opinion Mining, volume 5 of Synthesis Lectures on Human Language technologies. 2012.
[36] B. Liu and L. Zhang. A Survey of opinion mining and sentiment analysis, pages 415–463. Mining Text Data, 2012.
[37] Xudong Luo, Nicholas R Jennings, Nigel Shadbolt, Ho-fung Leung, and Jimmy Ho-man Lee. A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments. Artificial Intelligence, 148(1-2):53–102, 2003.
[38] Y.-W. Luo, S.-W. Tian, and L. Yu. Implicit sentiment analysis of uyghur text in opinion mining. Computer Engineering & Design, 35(9):3295–3300, 2014. (In Chinese).
[39] N. Majumder, S. Poria, D. Hazarika, R. Mihalcea, and et al. Dialoguernn: An attentive rnn for emotion detection in conversations. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, pages 6818–6825, 2019.
[40] P.S. Maria del, M. Jose, J.A. Paul, L. Katty, A.P. Mario, and V. Rafael. Sentiment analysis and trend detection in twitter. In Proceedings of the 2016 International Conference on Technologies and Innovation, pages 63–76, 2016.
[41] R. Martins, P.R. Henriques, and P. Novais. Determining emotional profile based on microblogging analysis. In Proceedings of the 19th Portuguese Conference on Artificial Intelligence, pages 159–171, 2019.
[42] M. Mcpherson and S. L. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27:415–444, 2001.
[43] M. Mertiya and A. Singh. Combining naive bayes and adjective analysis for sentiment detection on twitter. In Proceedings of the 2016 International Conference on Inventive Computation Technologies, pages 1–6, 2016.
[44] N. Mohebollah and J. Maktoubian. Business improvement approach based on sentiment twitter analysis: Case study. EAI Endorsed Transactions on Cloud Systems, 5(14):1–7, 2019.
[45] A.M. Mohsen, A. Idrees, and H.A. Hassan. Emotion analysis for opinion mining from text: A comparative study. International Journal of E-Collaboration, 15:38–58, 2019.
[46] L. Mostafa. Machine learning-based sentiment analysis for analyzing the travelers reviews on egyptian hotels. In Proceedings of the 2020 International Conference on Artificial Intelligence and Computer Vision, pages 405–413, 2020.
[47] M. Nakayama and Y. Wan. The cultural impact on social commerce: A sentiment analysis on yelp ethnic restaurant reviews. Information & Management, 56(2):271–279, 2019.
[48] T. Nasukawa and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture, pages 70–77, 2003.
[49] B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2):1–135, 2008.
[50] T. Patel, J. Undavia, and A. Patel. Sentiment analysis of parents feedback for educational institutes. International Journal of Innovative and Emerging Research in Engineering, 2(3):75–78, 2015.
[51] S. Poria, N. Majumder, R. Mihalcea, and E. Hovy. Emotion recognition in conversation: Research challenges, datasets, and recent advances. In Proceedings of IEEE Access, volume 7, pages 100943–100953, 2019.
[52] I. Prabha M and G. Umarani Srikanth. Survey of sentiment analysis using deep learning techniques. In Proceedings of the 1st International Conference on Innovations in Information and Communication Technology, pages 1–9, 2019.
[53] Anwar Ur Rehman, Ahmad Kamran Malik, Basit Raza, and Waqar Ali. A hybrid CNN-LSTM model for improving accuracy of movie reviews sentiment analysis. Multimedia Tools and Applications, 78(18):26597–26613, 2019.
[54] R. Ren, D.-S. Dash Wu, and T.-X. Liu. Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Systems Journal, 13(1):760–770, 2019.
[55] K. Sailunaz and R. Alhajj. Emotion and sentiment analysis from twitter text. Journal of Computational Science, 36:1–18, 2019.
[56] D. Sasikala and S. Sukumaran. A survey on lexicon and machine learning based classification methods for sentimental analysis. International Journal of Research and Analytical Reviews, 6(2):256–259, 2019.
[57] C.-L. Shen, L. Zhang, L.-Q. Wu, and S.-S. Li. Sentiment classification towards question-answering based on bidirectional attention mechanism. Computer Science, 46(7):151–156, 2019. (In Chinese).
[58] M. Soleymani, D. Garcia, B. Jou, B. Schuller, S.-F. Chang, and M. Pantic. A survey of multimodal sentiment analysis. Image and Vision Computing, 65:3–14, 2017.
[59] K.-S. Song, W. Gao, S. Feng, D.-L. Wang, and et al. Recommendation vs sentiment analysis: A text-driven latent factor model for rating prediction with cold-start awareness. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 2744–2750, 2017.
[60] K.-S. Song, W. Gao, L.-J. Zhao, C.-L. Sun, and X.-Z. Liu. Cold-start aware deep memory network for multi-entity aspect-based sentiment analysis. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 5179–5203, 2019.
[61] S.-Y. Song, C. Wang, C.-L. Chen, W. Zhou, and H.-Q. Chen. Sentiment analysis for intelligent customer service chatbots. Journal of Chinese Information Processing, 34(2):80–95, 2020. (In Chinese).
[62] L.-H. Sun, J.-P. Guo, and Y.-L. Zhu. Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online chinese reviews. World Wide Web, 22(1):83–100, 2019.
[63] L.-H. Sun and X.-F. Zhang. Improved collaborative filtering recommendation algorithm based on sentiment analysis of online review. Computer Science, 45(6A):402–405, 2018. (In Chinese).
[64] F. Tan, L. Li, Z.-Y. Zhang, and Y.-L. Guo. Latent co-interests’ relationship prediction. Tsinghua Science and Technology, 18(4):379–386, 2013. (In Chinese).
[65] D.-Y. Tang, B. Qin, and T. Liu. Aspect level sentiment classification with deep memory network. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 214–224, 2016.
[66] Y. Tay, L-A. Tuan, and S-C. Hui. Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, volume 18, pages 5956–5963, 2018.
[67] S. Tedmori and A. Awajan. Sentiment analysis main tasks and applications: A survey. Journal of Information Processing Systems, 15(3):500–519, 2019.
[68] L. Tussyadiah and F. Zach. Identifying salient attributes of peer-to-peer accommodation experience. Journal of Travel & Tourism Marketing, pages 1–17, 2016.
[69] R.-H. Wang, X.-M. Cui, W. Zhou, C.-L. Wang, and Y.-J. Li. Research of text sentiment classification based on improved semantic comprehension. Computer Science, 44(S2):92–97, 2017. (In Chinese).
[70] R.-W. Wang and W.-H. Zhang. Implicit evaluation object recognition method based on deep learning. Computer Engineering, 45(8):315–320, 2019. (In Chinese).
[71] Y.-Q. Wang, M.-L. Huang, X.-Y. Zhu, and Z. Li. Attention-based lstm for aspect-level sentiment classification. In Conference on Empirical Methods in Natural Language Processing, pages 606–615, 2017.
[72] R. Xia and Z.-X. Ding. Emotion-cause pair extraction: A new task to emotion analysis in texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1003–1012, 2019.
[73] G.-U. Xin. Design and implementation of customer service robot on wechat public platform. Information Technology, (5):166–169, 2017. (In Chinese).
[74] J.-C. Xu, D.-L. Chen, X.-P. Qiu, and X.-J. Huang. Cached long short-term memory neural networks for document-level sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1660–1669, 2016.
[75] H.-B. Yan, M.H. Ang Jr, and A. N. Poo. A survey on perception methods for humanrobot interaction in social robots. International Journal of Social Robotics, 6(1):85–119, 2014. (In Chinese).
[76] S.-L. Yeh, Y.-S. Lin, and C.-C. Lee. An interaction-aware attention network for speech emotion recognition in spoken dialogs. In Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, page 66856689, 2019.
[77] C.-M. Yu. Mining opinions from product review: Principles and algorithm analysis. Information Studies: Theory & Application, 32(7):124–128, 2009. (In Chinese).
[78] Jieyu Zhan, Xudong Luo, Cong Feng, and Minghua He. A multi-demand negotiation model based on fuzzy rules elicited via psychological experiments. Applied Soft Computing, 67:840–864, 2018.
[79] L. Zhang, S. Wang, and B. Liu. Deep learning for sentiment analysis: A survey. Data Mining and Knowledge Discovery, 8(4):1–25, 2018.
[80] M.-C. Zhang, R.-J. Zheng, J. Chen, and et al. Emotional component analysis and forecast public opinion on micro-blog posts based on maximum entropy model. Cluster Computing, 22(3):6295–6304, 2019.
[81] Y.-F. Zhang, G.-K. Lai, M. Zhang, Y. Zhang, Y.-Q. Liu, and S.-P. Ma. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th International ACM Sigir Conference on Research & Development in Information Retrieval, pages 83–92, 2014.
[82] X. Zhao, C. Huang, and H. Pan. Online comments of multi-category commodities based on emotional tendency analysis. Cluster Computing, 22(3):6345–6357, 2019.
[83] B.-Q. Zheng, H.-X. Zou, and X.-J. Hu. Research on public opinion prediction based on inflection point. Computer Science, 45(11A):539–541, 2018. (In Chinese).
[84] Q.-Y. Zhou, Z. Xu, and N.Y. Yen. User sentiment analysis based on social network information and its application in consumer reconstruction intention. Computers in Human Behavior, 100:177–183, 2019.