Skip to main content
SpringerLink
Log in

Sentiment Analysis and Opinion Mining

  • Chapter
  • First Online:
Text Data Mining

Abstract

With the rapid development and popularity of the World Wide Web, the Internet has entered the Web 2.0 and social network era. In Web 1.0, the Internet was characterized by static web pages. Web 2.0 refers to a World Wide Web that highlights user-generated content. Social networks are represented by a number of online tools and platforms, such as Twitter, Facebook, Weibo, and WeChat, where people share their perspectives, opinions, thoughts, and experiences. These online platforms contain innumerable subjective texts regarding different topics and events that fully reflect the individual opinions, sentiments, attitudes, and emotions of all of society.

Studying how to use computers to analyze, mine, and manage such subjective text is of great practical significance to nations, governments, enterprises, and individuals. National security agencies need to observe the content of these networks and identify possible responses, fraud, and invalid information dissemination to ensure network security; governments need to track public opinion to improve their policies and regulations; enterprises need to quickly understand the user’s opinions, comments, and suggestions based on network information to improve the quality of their products and services; when purchasing products, individuals want to quickly and accurately capture the pros and cons of products and make better choices and decisions.

Sentiment analysis, also called opinion mining, is the field of study that analyzes such subjectivities in texts. In this chapter, we will first introduce the categorization of sentiment analysis. Then, we will introduce sentiment analysis methods at different levels. We finally discuss two special issues in sentiment analysis, including polarity shift and domain adaptation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 89.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://trec.nist.gov/.

  2. 2.

    https://research.nii.ac.jp/ntcir/index-en.html.

  3. 3.

    https://en.m.wikipedia.org/wiki/SemEval.

References

  • Andreevskaia, A., & Bergler, S. (2006). Mining wordnet for a fuzzy sentiment: Sentiment tag extraction from wordnet glosses. In Proceedings of EACL (pp. 209–216).

    Google Scholar 

  • Aue, A., & Gamon, M. (2005). Customizing sentiment classifiers to new domains: A case study. In Proceedings of RANLP.

    Google Scholar 

  • Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of LREC (pp. 2200–2204).

    Google Scholar 

  • Bickel, S., Brückner, M., & Scheffer, T. (2009). Discriminative learning under covariate shift. Journal of Machine Learning Research, 10(9), 2137–2155.

    MathSciNet  MATH  Google Scholar 

  • Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G., & Reynar, J. (2008). Building a sentiment summarizer for local service reviews. In Proceedings of WWW Workshop Track (pp. 339–348).

    Google Scholar 

  • Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of ACL (pp. 440–447).

    Google Scholar 

  • Brody, S., & Elhadad, N. (2010). An unsupervised aspect-sentiment model for online reviews. In Proceedings of NAACL (pp. 804–812).

    Google Scholar 

  • Chen, P., Sun, Z., Bing, L., & Yang, W. (2017a). Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of EMNLP (pp. 452–461).

    Google Scholar 

  • Chernyshevich, M. (2014). IHS R&D belarus: Cross-domain extraction of product features using CRF. In Proceedings of SemEval (pp. 309–313).

    Google Scholar 

  • Choi, Y., & Cardie, C. (2008). Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of EMNLP (pp. 793–801).

    Google Scholar 

  • Cui, H., Mittal, V., & Datar, M. (2006). Comparative experiments on sentiment classification for online product reviews. In Proceedings of AAAI.

    Google Scholar 

  • Das, S., & Chen, M. (2001). Yahoo! for amazon: Extracting market sentiment from stock message boards. In Proceedings of APFA, Bangkok, Thailand (Vol. 35, p. 43)

    Google Scholar 

  • Das, S. R., & Chen, M. Y. (2007). Yahoo! for amazon: Sentiment extraction from small talk on the web. Management Science, 53, 1375–1388.

    Article  Google Scholar 

  • Dave, K., Lawrence, S., & Pennock, D. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of WWW (pp. 519–528).

    Google Scholar 

  • Ding, X., & Liu, B. (2007). The utility of linguistic rules in opinion mining. In Proceedings of SIGIR (pp. 811–812).

    Google Scholar 

  • Ding, X., Liu, B., & Yu, P. (2008). A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining (pp. 231–240).

    Google Scholar 

  • Ding, Y., Yu, J., & Jiang, J. (2017). Recurrent neural networks with auxiliary labels for cross-domain opinion target extraction. In Proceedings of AAAI (pp. 3436–3442).

    Google Scholar 

  • Ding, Z., He, H., Zhang, M., & Xia, R. (2019). From independent prediction to reordered prediction: Integrating relative position and global label information to emotion cause identification. In Proceedings of AAAI (Vol. 33, pp. 6343–6350).

    Google Scholar 

  • Ding, Z., Xia, R., & Yu, J. (2020). ECPE-2D: Emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. In Proceedings of ACL (pp. 3161–3170). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., & Xu, K. (2014). Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of ACL (pp. 49–54).

    Google Scholar 

  • Ekman, P., Friesen, W. V., & Ellsworth, P. (1972). Emotion in the human face: Guide-lines for research and an integration of findings: Guidelines for research and an integration of findings. Elmsford, NY: Pergamon.

    Google Scholar 

  • Esuli, A., & Sebastiani, F. (2007). Pageranking wordnet synsets: An application to opinion mining. In Proceedings of ACL (pp. 424–431).

    Google Scholar 

  • Gamon, M. (2004). Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of COLING (pp. 841–847). Stroudsburg: Association for Computational Linguistics.

    Chapter  Google Scholar 

  • Gui, L., Wu, D., Xu, R., Lu, Q., & Zhou, Y. (2016). Event-driven emotion cause extraction with corpus construction. In Proceedings of EMNLP (pp. 1639–1649). Singapore: World Scientific.

    Google Scholar 

  • Hatzivassiloglou, V., & McKeown, K. R. (1997). Predicting the semantic orientation of adjectives. In Proceedings of EACL (pp. 174–181). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of ACM SIGKDD (pp. 168–177).

    Google Scholar 

  • Huang, J., Gretton, A., Borgwardt, K., Schölkopf, B., & Smola, A. J. (2007). Correcting sample selection bias by unlabeled data. In Advances in Neural Information Processing Systems (pp. 601–608).

    Google Scholar 

  • Ikeda, D., Takamura, H., Ratinov, L., & Okumura, M. (2008). Learning to shift the polarity of words for sentiment classification. In Proceedings of IJCNLP (pp. 296–303).

    Google Scholar 

  • Irsoy, O., & Cardie, C. (2014). Deep recursive neural networks for compositionality in language. In Advances in Neural Information Processing Systems (pp. 2096–2104).

    Google Scholar 

  • Jakob, N., & Gurevych, I. (2010). Extracting opinion targets in a single and cross-domain setting with conditional random fields. In Proceedings of EMNLP (pp. 1035–1045). Cambridge, MA: Association for Computational Linguistics.

    Google Scholar 

  • Jiang, J., & Zhai, C. (2007). Instance weighting for domain adaptation in NLP. In Proceedings of ACL (pp. 264–271).

    Google Scholar 

  • Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011). Target-dependent twitter sentiment classification. In Proceedings of NAACL (pp. 151–160). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Jin, W., Ho, H. H., & Srihari, R. K. (2009). A novel lexicalized HMM-based learning framework for web opinion mining. In Proceedings of the 26th Annual International Conference on Machine Learning (Vol. 10). Citeseer.

    Google Scholar 

  • Jo, Y., & Oh, A. H. (2011). Aspect and sentiment unification model for online review analysis. In Proceedings of WSDM (pp. 815–824).

    Google Scholar 

  • Kamps, J., Marx, M., Mokken, R. J., & De Rijke, M. (2004). Using wordnet to measure semantic orientations of adjectives. In Proceedings of LREC (Vol. 4, pp. 1115–1118). Citeseer.

    Google Scholar 

  • Kanayama, H., & Nasukawa, T. (2006). Fully automatic lexicon expansion for domain-oriented sentiment analysis. In Proceedings of EMNLP (pp. 355–363).

    Google Scholar 

  • Kennedy, A., & Inkpen, D. (2006). Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2), 110–125.

    Article  MathSciNet  Google Scholar 

  • Kim, S.-M., & Hovy, E. (2004). Determining the sentiment of opinions. In Proceedings of COLING (pp. 1367–1373). Stroudsburg: Association for Computational Linguistics.

    Chapter  Google Scholar 

  • Kiritchenko, S., Zhu, X., Cherry, C., & Mohammad, S. (2014). NRC-Canada-2014: Detecting aspects and sentiment in customer reviews. In Proceedings of SemEval 2014 (pp. 437–442).

    Google Scholar 

  • Kobayashi, N., Inui, K., & Matsumoto, Y. (2007). Extracting aspect-evaluation and aspect-of relations in opinion mining. In Proceedings of EMNLP and CoNLL (pp. 1065–1074).

    Google Scholar 

  • Ku, L.-W., Liang, Y.-T., & Chen, H.-H. (2006). Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAI (pp. 100–107).

    Google Scholar 

  • Li, S., & Huang, C.-R. (2009). Sentiment classification considering negation and contrast transition. In Proceedings of PACLIC (pp. 307–316).

    Google Scholar 

  • Li, S., Lee, S. Y., Chen, Y., Huang, C.-R., & Zhou, G. (2010a). Sentiment classification and polarity shifting. In Proceedings of COLING (pp. 635–643).

    Google Scholar 

  • Li, S., Xia, R., Zong, C., & Huang, C.-R. (2009a). A framework of feature selection methods for text categorization. In Proceedings of ACL-IJCNLP (pp. 692–700).

    Google Scholar 

  • Li, T., Zhang, Y., & Sindhwani, V. (2009b). A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge. In Proceedings of ACL-IJCNLP (pp. 244–252). Stroudsburg: Association for Computational Linguistics.

    Chapter  Google Scholar 

  • Li, X., Dong, Y., & Li, J. (2010b). Data mining and knowledge discovering. Beijing: High Education Press (in Chinese).

    Google Scholar 

  • Li, X., & Lam, W. (2017). Deep multi-task learning for aspect term extraction with memory interaction. In Proceedings of EMNLP (pp. 616–626).

    Google Scholar 

  • Li, Z., Wei, Y., Zhang, Y., & Yang, Q. (2018). Hierarchical attention transfer network for cross-domain sentiment classification. In Proceedings of AAAI (pp. 5852–5859).

    Google Scholar 

  • Li, Z., Zhang, Y., Wei, Y., Wu, Y., & Yang, Q. (2017c). End-to-end adversarial memory network for cross-domain sentiment classification. In Proceedings of IJCAI (pp. 2237–2243).

    Google Scholar 

  • Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In Proceedings of CIKM (pp. 375–384).

    Google Scholar 

  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.

    Article  Google Scholar 

  • Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Liu, J., & Zhang, Y. (2017). Attention modeling for targeted sentiment. In Proceedings of EACL (pp. 572–577).

    Google Scholar 

  • Liu, P., Joty, S., & Meng, H. (2015b). Fine-grained opinion mining with recurrent neural networks and word embeddings. In Proceedings of EMNLP (pp. 1433–1443).

    Google Scholar 

  • Ma, D., Li, S., Zhang, X., & Wang, H. (2017). Interactive attention networks for aspect-level sentiment classification. In Proceedings of IJCAI (pp. 4068–4074).

    Google Scholar 

  • Mao, Y., & Lebanon, G. (2007). Isotonic conditional random fields and local sentiment flow. In Advances in Neural Information Processing Systems (pp. 961–968).

    Google Scholar 

  • McDonald, R., Hannan, K., Neylon, T., Wells, M., & Reynar, J. (2007). Structured models for fine-to-coarse sentiment analysis. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (pp. 432–439). Prague: Association for Computational Linguistics.

    Google Scholar 

  • Mei, Q., Ling, X., Wondra, M., Su, H., & Zhai, C. (2007). Topic sentiment mixture: Modeling facets and opinions in weblogs. In Proceedings of WWW (pp. 171–180).

    Google Scholar 

  • Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., & Cherry, C. (2016). SemEval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (pp. 31–41). San Diego, CA: Association for Computational Linguistics.

    Google Scholar 

  • Mohammad, S., Kiritchenko, S., & Zhu, X. (2013). NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In Proceedings of SemEval (pp. 321–327). Atlanta, GA: Association for Computational Linguistics.

    Google Scholar 

  • Mukherjee, A., & Liu, B. (2012). Aspect extraction through semi-supervised modeling. In Proceedings of ACL (pp. 339–348). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Mullen, T., & Collier, N. (2004). Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP (pp. 412–418).

    Google Scholar 

  • Na, J.-C., Sui, H., Khoo, C. S., Chan, S., & Zhou, Y. (2004). Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews. In Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pp. 49–54).

    Google Scholar 

  • Nakagawa, T., Inui, K., & Kurohashi, S. (2010). Dependency tree-based sentiment classification using CRFs with hidden variables. In Proceedings of NAACL (pp. 786–794).

    Google Scholar 

  • Ng, V., Dasgupta, S., & Arifin, S. N. (2006). Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of COLING/ACL (pp. 611–618).

    Google Scholar 

  • Orimaye, S. O., Alhashmi, S. M., & Siew, E.-G. (2012). Buy it-don’t buy it: Sentiment classification on amazon reviews using sentence polarity shift. In Proceedings of PRICAI (pp. 386–399). Berlin: Springer.

    Google Scholar 

  • Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010a). Cross-domain sentiment classification via spectral feature alignment. In Proceedings of WWW (pp. 751–760).

    Google Scholar 

  • Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2010b). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 199–210.

    Article  Google Scholar 

  • Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  • Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of ACL (pp. 271–278). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.

    Article  Google Scholar 

  • Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP (pp. 79–86). Stroudsburg: Association for Computational Linguistics.

    Chapter  Google Scholar 

  • Plutchik, R., & Kellerman, H. (1986). Emotion: Theory, research and experience. Volume 3 in biological foundations of emotions. Oxford: Pergamon.

    Google Scholar 

  • Polanyi, L., & Zaenen, A. (2006). Contextual valence shifters. In Computing attitude and affect in text: Theory and applications (pp. 1–10). Berlin: Springer.

    Google Scholar 

  • Popescu, A.-M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9–28). Berlin: Springer.

    Chapter  Google Scholar 

  • Qian, Q., Huang, M., Lei, J., & Zhu, X. (2017). Linguistically regularized LSTM for sentiment classification. In Proceedings of ACL (pp. 1679–1689).

    Google Scholar 

  • Qiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion word expansion and target extraction through double propagation. Computational Linguistics, 37(1), 9–27.

    Article  Google Scholar 

  • Shimodaira, H. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2), 227–244.

    Article  MathSciNet  MATH  Google Scholar 

  • Snyder, B., & Barzilay, R. (2007). Multiple aspect ranking using the good grief algorithm. In Proceedings of NAACL (pp. 300–307). Rochester, NY: Association for Computational Linguistics.

    Google Scholar 

  • Socher, R., Huval, B., Manning, C. D., & Ng, A. Y. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of EMNLP (pp. 1201–1211). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Socher, R., Lin, C. C., Manning, C., & Ng, A. Y. (2011a). Parsing natural scenes and natural language with recursive neural networks. In Proceedings of ICML (pp. 129–136).

    Google Scholar 

  • Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., et al.(2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1631–1642).

    Google Scholar 

  • Strapparava, C., & Valitutti, A. (2004). Wordnet affect: An affective extension of wordnet. In Proceedings of LREC (Vol. 4, p. 40). Citeseer.

    Google Scholar 

  • Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P. V., & Kawanabe, M. (2008). Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in Neural Information Processing Systems (pp. 1433–1440).

    Google Scholar 

  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307.

    Article  Google Scholar 

  • Tai, K. S., Socher, R., & Manning, C. D. (2015). Improved semantic representations from tree-structured long short-term memory networks. Proceedings of ACL and IJCNLP (pp. 1556–1565).

    Google Scholar 

  • Tang, D., Qin, B., Feng, X., & Liu, T. (2015a). Effective LSTMs for target-dependent sentiment classification. Proceedings of COLING (pp. 3298–3307).

    Google Scholar 

  • Tang, D., Qin, B., & Liu, T. (2015b). Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 1422–1432).

    Google Scholar 

  • Tang, D., Qin, B., & Liu, T. (2016). Aspect level sentiment classification with deep memory network. Proceedings of EMNLP (pp. 3298–3307).

    Google Scholar 

  • Tang, D., Wei, F., Qin, B., Zhou, M., & Liu, T. (2014a). Building large-scale twitter-specific sentiment lexicon: A representation learning approach. In Proceedings of COLING (pp. 172–182).

    Google Scholar 

  • Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014b). Learning sentiment-specific word embedding for twitter sentiment classification. In Proceedings of ACL (pp. 1555–1565).

    Google Scholar 

  • Thet, T. T., Na, J.-C., & Khoo, C. S. G. (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science, 36, 823–848.

    Article  Google Scholar 

  • Titov, I., & McDonald, R. (2008). A joint model of text and aspect ratings for sentiment summarization. In Proceedings of ACL (pp. 308–316). Columbus, OH: Association for Computational Linguistics.

    Google Scholar 

  • Toh, Z., & Wang, W. (2014). DLIREC: Aspect term extraction and term polarity classification system. In Association for Computational Linguistics and Dublin City University (pp. 235–240). Citeseer.

    Google Scholar 

  • Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL (pp. 417–424). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Turney, P. D., & Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4):315–346.

    Article  Google Scholar 

  • Vo, D.-T., & Zhang, Y. (2015). Target-dependent twitter sentiment classification with rich automatic features. In Proceedings of IJCAI (pp. 1347–1353).

    Google Scholar 

  • Vo, D. T., & Zhang, Y. (2016). Don’t count, predict! an automatic approach to learning sentiment lexicons for short text. In Proceedings of ACL (pp. 219–224). Berlin: Association for Computational Linguistics.

    Google Scholar 

  • Wang, K., & Xia, R. (2015). An approach to Chinese sentiment lexicon construction based on conjunction relation. In Proceedings of CCL.

    Google Scholar 

  • Wang, K., & Xia, R. (2016). A survey on automatical construction methods of sentiment lexicons. Acta Automatica Sinica, 42(4), 495–511.

    Google Scholar 

  • Wang, L., & Xia, R. (2017). Sentiment lexicon construction with representation learning based on hierarchical sentiment supervision. In Proceedings of EMNLP (pp. 502–510).

    Google Scholar 

  • Wang, W. Y., Mehdad, Y., Radev, D. R., & Stent, A. (2016a). A low-rank approximation approach to learning joint embeddings of news stories and images for timeline summarization. In Proceedings of ACL (pp. 58–68).

    Google Scholar 

  • Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016c). Attention-based LSTM for aspect-level sentiment classification. In Proceedings of EMNLP (pp. 606–615).

    Google Scholar 

  • Whitehead, M., & Yaeger, L. (2010). Sentiment mining using ensemble classification models. In Innovations and advances in computer sciences and engineering (pp. 509–514). Berlin: Springer.

    Chapter  Google Scholar 

  • Whitelaw, C., Garg, N., & Argamon, S. (2005). Using appraisal groups for sentiment analysis. In Proceedings of CIKM (pp. 625–631).

    Google Scholar 

  • Wiebe, J., Wilson, T., Bruce, R., Bell, M., & Martin, M. (2004). Learning subjective language. Computational Linguistics, 30(3), 277–308.

    Article  Google Scholar 

  • Wiebe, J. M., Bruce, R. F., & O’Hara, T. P. (1999). Development and use of a gold-standard data set for subjectivity classifications. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (pp. 246–253). College Park, MD: Association for Computational Linguistics.

    Google Scholar 

  • Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT-EMNLP (pp. 347–354).

    Google Scholar 

  • Xia, R., & Ding, Z. (2019). Emotion-cause pair extraction: A new task to emotion analysis in texts. In Proceedings of ACL (pp. 1003–1012).

    Google Scholar 

  • Xia, R., Hu, X., Lu, J., Yang, J., & Zong, C. (2013a). Instance selection and instance weighting for cross-domain sentiment classification via PU learning. In Proceedings of IJCAI (pp. 2176–2182).

    Google Scholar 

  • Xia, R., Pan, Z., & Xu, F. (2018). Instance weighting for domain adaptation via trading off sample selection bias and variance. In Proceedings of IJCAI (pp. 4489–4495). Palo Alto, CA: AAAI Press.

    Google Scholar 

  • Xia, R., Wang, C., Dai, X., & Li, T. (2015a). Co-training for semi-supervised sentiment classification based on dual-view bags-of-words representation. In Proceedings of ACL-IJCNLP (pp. 1054–1063).

    Google Scholar 

  • Xia, R., Wang, T., Hu, X., Li, S., & Zong, C. (2013b). Dual training and dual prediction for polarity classification. In Proceedings of ACL (pp. 521–525).

    Google Scholar 

  • Xia, R., Xu, F., Yu, J., Qi, Y., & Cambria, E. (2016). Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis. Information Processing & Management, 52(1), 36–45.

    Article  Google Scholar 

  • Xia, R., Xu, F., Zong, C., Li, Q., Qi, Y., & Li, T. (2015b). Dual sentiment analysis: Considering two sides of one review. IEEE Transactions on Knowledge and Data Engineering, 27(8), 2120–2133.

    Article  Google Scholar 

  • Xia, R., Yu, J., Xu, F., & Wang, S. (2014). Instance-based domain adaptation in NLP via in-target-domain logistic approximation. In Proceedings of AAAI (pp. 1600–1606).

    Google Scholar 

  • Xia, R., Zhang, M., & Ding, Z. (2019). RTHN: A RNN-transformer hierarchical network for emotion cause extraction. In Proceedings of IJCAI (pp. 5285–5291). Palo Alto, CA: AAAI Press.

    Google Scholar 

  • Xia, R., & Zong, C. (2011). A POS-based ensemble model for cross-domain sentiment classification. In Proceedings of IJCNLP (pp. 614–622).

    Google Scholar 

  • Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181(6), 1138–1152.

    Article  Google Scholar 

  • Xue, G.-R., Dai, W., Yang, Q., & Yu, Y. (2008). Topic-bridged PLSA for cross-domain text classification. In Proceedings of SIGIR (pp. 627–634).

    Google Scholar 

  • Yu, J., & Jiang, J. (2016). Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. In Proceedings of EMNLP (pp. 236–246).

    Google Scholar 

  • Yu, J., Zha, Z.-J., Wang, M., & Chua, T.-S. (2011). Aspect ranking: Identifying important product aspects from online consumer reviews. In Proceedings of NAACL (pp. 1496–1505). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Zadrozny, B. (2004). Learning and evaluating classifiers under sample selection bias. In Proceedings of ICML (pp. 114–121).

    Google Scholar 

  • Zhang, M., Zhang, Y., & Vo, D.-T. (2016b). Gated neural networks for targeted sentiment analysis. In Proceedings of AAAI (pp. 3087–3093).

    Google Scholar 

  • Zhao, W. X., Jiang, J., Yan, H., & Li, X. (2010). Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In Proceedings of EMNLP (pp. 56–65). Stroudsburg: Association for Computational Linguistics.

    Google Scholar 

  • Zhuang, L., Jing, F., & Zhu, X.-Y. (2006). Movie review mining and summarization. In Proceedings of CIKM (pp. 43–50). New York, NY: Association for Computing Machinery.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, Beijing, China

    Chengqing Zong & Jiajun Zhang

  2. School of Computer Science & Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Rui Xia

Authors
  1. Chengqing Zong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiajun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Tsinghua University Press

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zong, C., Xia, R., Zhang, J. (2021). Sentiment Analysis and Opinion Mining. In: Text Data Mining. Springer, Singapore. https://doi.org/10.1007/978-981-16-0100-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-0100-2_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0099-9

  • Online ISBN: 978-981-16-0100-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation