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Sentiment Classification Using Recurrent Neural Network

  • Kavita MoholkarEmail author
  • Krupa RathodEmail author
  • Krishna RathodEmail author
  • Mritunjay TomarEmail author
  • Shashwat RaiEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)

Abstract

Sentiment basically represents a person’s attitude, expressing thoughts or an expression triggered by a feeling. Sentiment analysis is the study of sentiments on a given piece of text. Users can express their sentiment/thoughts on internet which may have impact on the user reading it [7]. This expressed sentiment are usually available in unstructured format which needs to be converted. Sentiment analysis is referred to as organizing text into a structured format [7]. The challenge for sentiment analysis is insufficient labelled information, this can be overcome by using machine learning algorithms. Therefore, to perform sentiment analysis we have employed Deep Neural Network.

Keywords

Sentiment analysis: sentiment polarity Deep Neural Networks RNN LSTM 

References

  1. 1.
    Swapna, G., Soman, K.P., VinayKumar, R.: Automated detection of cardiac arrhythmia using deep learning techniques. Procedia Comput. Sci. 132, 1192–1201 (2018)CrossRefGoogle Scholar
  2. 2.
    Luo, Y.: Recurrent neural network for classifying relations in clinical notes. J. Biomed. Informat. 72, 85–95 (2017)CrossRefGoogle Scholar
  3. 3.
    Rao, G., Huang, W., Feng, Z., Cong, Q.: LSTM with sentence representation for document-level sentiment classification. Neurocomputing 208, 49–57 (2018)CrossRefGoogle Scholar
  4. 4.
    Khosla, E., Ramesh, D., Sharma, P.P., Nyakotey, S.: RNN’s-RT: flood based prediction of Human and animal deaths in Bihar using recurrent neural networks and regression techniques. Procedia Comput. Sci. 132, 486–497 (2018)CrossRefGoogle Scholar
  5. 5.
    Kumar, J., Goomer, R., Singh, A.K.: Long short-term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters. Procedia Comput. Sci. 12, 676–682 (2018)CrossRefGoogle Scholar
  6. 6.
    Arifoglu, D., Bouchachia, A.: Activity recognition and abnormal behavior detection with recurrent neural networks. In: The 14th International Conference on Mobile Systems and Pervasive ComputingGoogle Scholar
  7. 7.
    Ain, Q.T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., Rehman, A.: Sentiment analysis using deep learning techniques: A review. IJACSA 8(6), 424 (2017)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    Islam, J., Zhang, Y.: Visual sentiment analysis for social images using transfer learning approach. In: 2016 IEEE International Conference on Big Data Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing Communications, pp. 124–130 (2016)Google Scholar
  11. 11.
    Ouyang, X., Zhou, P., Li, C.H., Liu, L.: Sentiment analysis using convolutional neural network. In: 2015 IEEE International Conference on Computer and Information Technology Ubiquitous Computing and Communications Dependable, Autonomic Secure Computing Pervasive Intelligence Computing (CIT/IUCC/DASC/PICOM), pp. 2359–2364 (2015)Google Scholar
  12. 12.
    Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z.: Artificial intelligence perspectives in intelligent systems. In: Proceedings of the 5th Computer Science On-line Conference 2016 (CSOC2016), vol 1, Advances in Intelligent Systems and Computing, vol. 464, pp. 249–261 (2016)Google Scholar
  13. 13.
    Vateekul, P., Koomsubha, T.: A study of sentiment analysis using deep learning techniques on Thai Twitter Data (2016)Google Scholar
  14. 14.
    Yanagimoto, H., Shimada, M., Yoshimura, A.: Document similarity estimation for sentiment analysis using neural network. In: 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, pp. 105–110 (2013)Google Scholar
  15. 15.
    Ain, Q.T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., Rehman, A.: Sentiment analysis using deep learning techniques: a review.Google Scholar
  16. 16.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: 42nd Meeting of the Association for Computational Linguistics (ACL 2004), 271–278 (2004)Google Scholar
  17. 17.
    Luo, Z., Osborne, M., Wang, T.: An effective approachto tweets opinion retrieval. World Wide Web (2013).  https://doi.org/10.1007/s11280-013-0268-7CrossRefGoogle Scholar
  18. 18.
    Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment Treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2013)Google Scholar
  19. 19.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexiconbasedmethods for sentiment analysis. Comput. linguis. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  20. 20.
    Wan, X.: A comparative study of cross-lingual sentiment classification. In: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 24–31). IEEE Computer Society (2012)Google Scholar
  21. 21.
    Bollegala, D., Weir, D., Carroll, J.: Cross-Domain SentimentClassification using a Sentiment Sensitive Thesaurus. IEEE Trans. Knowl. Data Eng. 25(8), 1719–1731 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringJSPM’s Rajarshi Shahu College of EngineeringPuneIndia

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