Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

Sentiment analysis analyses people’s viewpoints, feelings, assessments, behaviour and psychology towards living and abstract entities. It highlights viewpoints which present positively or negatively biased sentiments.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)

    Article  Google Scholar 

  2. Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments and Emotions. Cambridge University Press (2015)

    Google Scholar 

  3. World travel and tourism council, travel and tourism economic impart 2016: www.wttc.org/-/media/files/reports/economic%20impact%20research/regions%202016/world2016.pdf

  4. O’Connor, P.: User-generated content and travel: a case study on TripAdvisor.com. In: O’Connor, P., Höpken, W., Gretzel, U. (eds.) Information and Communication Technologies in Tourism, pp. 47–48. Springer (2008)

    Google Scholar 

  5. Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of web services. Inf. Sci. 311(2), 18–38 (2015)

    Article  Google Scholar 

  6. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)

    Google Scholar 

  7. Shouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2016)

    Article  Google Scholar 

  8. Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the K-CAP ’03, 2nd International Conference on Knowledge Capture, pp. 70–77 (2003)

    Google Scholar 

  9. Hatzivassiloglou, V., Mckeown, K. R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, pp. 174–181 (1997)

    Google Scholar 

  10. Hearst, M. A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational linguistics, vol. 2, pp. 539–545 (1992)

    Google Scholar 

  11. Wiebe, J.: Recognizing subjective sentences: a computational investigation of narrative text. PhD Thesis, State University of New York at Buffalo (1990)

    Google Scholar 

  12. Wiebe, J.: Tracking point of view in narrative. Comput. Linguist. 20(2), 233–287 (1994)

    Google Scholar 

  13. Wiebe, J., Bruce, R., O’Hara, T.: 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 (1999)

    Google Scholar 

  14. Wiebe, J.: Learning subjective adjectives from corpora. In: Proceedings of the 17th National Conference on Artificial Intelligence, pp. 735–740 (2000)

    Google Scholar 

  15. Das, S.R., Chen, M.Y.: Yahoo! for Amazon: sentiment extraction from small talk on web. Manage. Sci. 53(9), 1375–1388 (2007)

    Article  Google Scholar 

  16. Satoshi, M., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the web. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 341–349 (2002)

    Google Scholar 

  17. Bo, P., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86 (2002)

    Google Scholar 

  18. Tong, R.: An operational system for detecting and tracking opinions in on-line discussions. In: Working Notes of the SIGIR Workshop on Operational Text Classification, pp. 1–6 (2001)

    Google Scholar 

  19. Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424 (2002)

    Google Scholar 

  20. Chaudhuri, A.: Some significant works in sentiment analysis during the past decade. Technical Report, TH-7050, Birla Institute of Technology Mesra, Patna Campus (2014)

    Google Scholar 

  21. Chaudhuri, A., Ghosh, S. K.: Sentiment analysis of customer reviews using robust hierarchical bidirectional recurrent neural networks. In: Silhavy, R. et al. (eds.) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 464, pp. 249–261. Springer (2016)

    Google Scholar 

  22. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Proceedings of the Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, vol. 10, pp. 178–185 (2010)

    Google Scholar 

  23. Borth, D., Chen, T., Ji, R., Chang. S.F.: Sentibank: large scale ontology and classifiers for detecting sentiment and emotions in visual content. In: Proceedings of the 21st ACM Conference on Multimedia, pp. 459–460 (2013)

    Google Scholar 

  24. Morency, L.P., Mihalcea, R., Doshi, P.: Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the International Conference on Multimodal Interfaces, pp. 169–176 (2011)

    Google Scholar 

  25. You, Q., Luo, J., Jin, H., Yang, J.: Cross modality consistent regression for joint visual textual sentiment analysis of social media. In: Proceedings of the 9th International Conference on Web Search and Data Mining, pp. 13–22 (2016)

    Google Scholar 

  26. You, Q., Luo, J., Jin, H., Yang, J.: Joint visual textual sentiment analysis with deep neural networks. In: Proceedings of the 23rd ACM Conference on Multimedia, pp. 1071–1074 (2015)

    Google Scholar 

  27. You, Q., Cao, L., Jin, H., Luo, J.: Robust visual textual sentiment analysis: when attention meets tree structured recurrent neural networks. In: Proceedings of the 24th ACM Conference on Multimedia, pp. 1008–1017 (2016)

    Google Scholar 

  28. McGlohon, M., Glance, N., Reiter, Z.: Star quality: aggregating reviews to rank products and merchants. In: Proceedings of the 4th Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 144–121 (2010)

    Google Scholar 

  29. Hong, Y., Skiena, S.: The wisdom of bookies? Sentiment analysis vs. the NFL point spread. In: Proceedings of the 4th Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 251–254 (2010)

    Google Scholar 

  30. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the 4th Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 122–129 (2010)

    Google Scholar 

  31. Bi, C., Zhu, L., Kifer, D., Lee, D.: What is an opinion about? Exploring political standpoints using opinion scoring model. In: Proceedings of the 4th Association for the Advancement in Artificial Intelligence Conference on Artificial Intelligence, pp. 1007–1012 (2010)

    Google Scholar 

  32. Yano, T., Smith, N.A.: What’s worthy of comment? Content and comment volume in political blogs. In: Proceedings of the 4th Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 359–362 (2010)

    Google Scholar 

  33. Sitaram, A., Huberman, B.A.: Predicting the future with social media. arXiv:1003.5699. (2010)

    Google Scholar 

  34. Joshi, M., Das, D., Gimpel, K., Smith, N.A.: Movie reviews and revenues: an experiment in text regression. In: Proceedings of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Conference, pp. 293–296 (2010)

    Google Scholar 

  35. Sadikov, E., Parameswaran, A., Venetis, P.: Blogs as predictors of movie success. In: Proceedings of the 3rd International Conference on Weblogs and Social Media, pp. 304–307 (2009)

    Google Scholar 

  36. Miller, M., Sathi, C., Wiesenthal, D., Leskovec, J., Potts, C.: Sentiment flow through hyperlink networks. In: Proceedings of the 5th International Association for Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 550–553 (2011)

    Google Scholar 

  37. Mohammad, S., Yang, T.: Tracking sentiment in mail: how genders differ on emotional axes. In: Proceedings of the ACL Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 70–79 (2011)

    Google Scholar 

  38. Mohammad, S.: From once upon a time to happily ever after: tracking emotions in novels and fairy tales. In: Proceedings of the ACL Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 105–114 (2011)

    Google Scholar 

  39. Bollen, J., Mao, H., Zeng, X.J.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  40. Roy, B.H., Dinur, E., Feldman, R., Fresko, M., Goldstein, G.: Identifying and following expert investors in stock microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1310–1319 (2011)

    Google Scholar 

  41. Feldman, R., Rosenfeld, B., Roy B.H., Fresko, M.: The stock sonar—sentiment analysis of stocks based on a hybrid approach. In: Proceedings of the 23rd Innovative Applications of Artificial Intelligence Conference, pp. 1642–1647 (2011)

    Google Scholar 

  42. Zhang, W., Skiena, S.: Trading strategies to exploit blog and news sentiment. In: Proceedings of the 4th International Conference on Weblogs and Social Media, pp. 375–378 (2010)

    Google Scholar 

  43. Sakunkoo, P., Sakunkoo, N.: Analysis of social influence in online book reviews. In: Proceedings of the 3rd Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 308–310 (2009)

    Google Scholar 

  44. Groh, G., Hauffa, J.: Characterizing social relations via NLP-based sentiment analysis. In: Proceedings of the 5th Association for the Advancement in Artificial Intelligence Conference on Weblogs and Social Media, pp. 502–505 (2011)

    Google Scholar 

  45. Castellanos, M., Dayal, U., Hsu, M., Ghosh, R., Dekhil, M., Lu, Y., Zhang, L., Schreiman, M.: LCI: a social channel analysis platform for live customer intelligence. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1049–1058 (2011)

    Google Scholar 

  46. Pozzi, F.A.: Sentiment analysis in social networks. Morgan Kaufmann (2016)

    Google Scholar 

  47. Karthik, S., Paul, A., Karthikeyan, N.: Deep learning innovations and their convergence with big data. In: Advances in Data Mining and Database Management. IGI Global (2017)

    Google Scholar 

  48. Chaudhuri, A., Ghosh, S.K.: Bankruptcy prediction through soft computing based deep learning technique. Springer Nature (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Samsung R & D Institute Delhi, Noida, India

    Arindam Chaudhuri

Authors
  1. Arindam Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arindam Chaudhuri .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s), under exclusive to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chaudhuri, A. (2019). Introduction. In: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-7474-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7474-6_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7473-9

  • Online ISBN: 978-981-13-7474-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation