Skip to main content

Deep Ensemble Model with the Fusion of Character, Word and Lexicon Level Information for Emotion and Sentiment Prediction

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11305)


In this paper, we propose a novel neural network based architecture which incorporates character, word and lexicon level information to predict the degree of intensity for sentiment and emotion. At first we develop two deep learning models based on Long Short Term Memory (LSTM) & Convolutional Neural Network (CNN), and a feature based model. Each of these models takes as input a fusion of various representations obtained from the characters, words and lexicons. A Multi-Layer Perceptron (MLP) network based ensemble model is then constructed by combining the outputs of these three models. Evaluation on the benchmark datasets related to sentiment and emotion shows that our proposed model attains the state-of-the-art performance.


  • Sentiment analysis
  • Emotion analysis
  • Intensity Prediction
  • Financial domain
  • Ensemble
  • Deep learning

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Learn about institutional subscriptions


  1. 1.

    We also call this problem as fine-grained emotion & sentiment analysis.

  2. 2.

    Spell Checker Oriented Word Lists (SCOWL):


  1. Akhtar, M.S., Kumar, A., Ghosal, D., Ekbal, A., Bhattacharyya, P.: A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 540–546 (2017)

    Google Scholar 

  2. Akhtar, M.S., Sikdar, U.K., Ekbal, A.: IITP: hybrid approach for text normalization in Twitter. In: Proceedings of the ACL 2015 Workshop on Noisy User-generated Text (WNUT-2015), Beijing, China, pp. 106–110 (2015)

    Google Scholar 

  3. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)

    Google Scholar 

  4. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., Newton (2009)

    MATH  Google Scholar 

  5. Bravo-Marquez, F., Frank, E., Mohammad, S.M., Pfahringer, B.: Determining word-emotion associations from tweets by multi-label classification. In: WI 2016, pp. 536–539. IEEE Computer Society (2016)

    Google Scholar 

  6. Cambria, E., Poria, S., Bajpai, R., Schuller, B.: Senticnet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2666–2677 (2016)

    Google Scholar 

  7. Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., Zarrouk, M., Davis, B.: Semeval-2017 task 5: fine-grained sentiment analysis on financial microblogs and news. In: Proceedings of the 11th International Workshop on SemEval-2017. ACL, Vancouver (2017)

    Google Scholar 

  8. Davidson, R.J., Sherer, K.R., Goldsmith, H.H.: Handbook of Affective Sciences. Oxford University Press, Oxford (2009)

    Google Scholar 

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

    Google Scholar 

  10. Ekman, P.: An argument for basic emotions. Cogn. Emot. 6, 169–200 (1992)

    CrossRef  Google Scholar 

  11. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Aistats, vol. 15, p. 275 (2011)

    Google Scholar 

  12. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  13. Jain, P., Goel, P., Kulshreshtha, D., Shukla, K.K.: Prayas at EmoInt 2017: an ensemble of deep neural architectures for emotion intensity prediction in tweets. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 58–65. ACL, Copenhagen, September 2017

    Google Scholar 

  14. Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models (2016)

    Google Scholar 

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR arxiv:abs/1412.6980 (2014)

  16. Köper, M., Kim, E., Klinger, R.: IMS at EmoInt-2017: emotion intensity prediction with affective norms, automatically extended resources and deep learning. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 50–57. ACL, Copenhagen, September 2017

    Google Scholar 

  17. Lan, M., Jiang, M., Wu, Y.: ECNU at SemEval-2017 task 5: an ensemble of regression algorithms with effective features for fine-grained sentiment analysis in financial domain. In: Proceedings of the 11th International Workshop on SemEval-2017, ACL, Vancouver (2017)

    Google Scholar 

  18. Mansar, Y., Gatti, L., Ferradans, S., Guerini, M., Staiano, J.: Fortia-FBK at SemEval-2017 task 5: bullish or bearish? Inferring sentiment towards brands from financial news headlines. In: Proceedings of the 11th International Workshop on SemEval-2017. ACL, Vancouver, Canada (2017)

    Google Scholar 

  19. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  20. Mohammad, S., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the Seventh International Workshop on Semantic Evaluation Exercises (SemEval-2013), Atlanta, Georgia, USA, June 2013

    Google Scholar 

  21. Mohammad, S.M., Bravo-Marquez, F.: WASSA-2017 shared task on emotion intensity. In: Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), Copenhagen, Denmark (2017)

    Google Scholar 

  22. Mohammad, S.M., Kiritchenko, S.: Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31(2), 301–326 (2015)

    CrossRef  MathSciNet  Google Scholar 

  23. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    CrossRef  MathSciNet  Google Scholar 

  24. Nielsen, F.Å.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903 (2011)

  25. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    CrossRef  Google Scholar 

  26. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  27. Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)

    CrossRef  Google Scholar 

  28. Russell, J.A., Barrett, L.F.: Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. J. Pers. Soc. Psychol. 76(5), 805 (1999)

    CrossRef  Google Scholar 

  29. Wiebe, J., Mihalcea, R.: Word sense and subjectivity. In: Proceedings of the COLING/ACL, Australia, pp. 1065–1072 (2006)

    Google Scholar 

Download references


Asif Ekbal acknowledges Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Md Shad Akhtar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosal, D., Akhtar, M.S., Ekbal, A., Bhattacharyya, P. (2018). Deep Ensemble Model with the Fusion of Character, Word and Lexicon Level Information for Emotion and Sentiment Prediction. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)