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Analytical Approach for Sentiment Analysis of Movie Reviews Using CNN and LSTM

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Artificial Intelligence and Speech Technology (AIST 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1546))

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Abstract

With the rapid growth of technology and easier access to the internet, several forums like Twitter, Facebook, Instagram, etc., have come up, providing people with a space to express their opinions and reviews about anything and everything happening in the world. Movies are widely appreciated and criticized art forms. They are a significant source of entertainment and lead to web forums like IMDB and amazon reviews for users to give their feedback about the movies and web series. These reviews and feedback draw incredible consideration from scientists and researchers to capture the vital information from the data. Although this information is unstructured, it is very crucial. Deep learning and machine learning have grown as powerful tools examining the polarity of the sentiments communicated in the review, known as ‘opinion mining’ or ‘sentiment classification.’ Sentiment analysis has become the most dynamic exploration in NLP (natural language processing) as text frequently conveys rich semantics helpful for analyzing. With ongoing advancement in deep learning, the capacity to analyze this content has enhanced significantly. Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) is primarily implemented as powerful deep learning techniques in Natural Language Processing tasks. This study covers an exhaustive study of sentiment analysis of movie reviews using CNN and LSTM by elaborating the approaches, datasets, results, and limitations.

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References

  1. Daeli, N.O., Adiwijaya, A.: Sentiment analysis on movie reviews using information gain and K-nearest neighbor. J. Data Sci. Appl. 3(1), 1–7 (2020)

    Google Scholar 

  2. Lakshmi, B.S., Raj, P.S., Vikram, R.R.: Sentiment analysis using deep learning technique CNN with KMeans. Int. J. Pure Appl. Math. 114(11), 47–57 (2017)

    Google Scholar 

  3. Bodapati, J.D., Veeranjaneyulu, N., Shaik, S.: Sentiment analysis from movie reviews using LSTMs. Ingenierie des Systemes d’Information 24(1), 125–129 (2019)

    Google Scholar 

  4. Pouransari, H., Ghili, S.: Deep learning for sentiment analysis of movie reviews. CS224N Proj. 1–8 (2014)

    Google Scholar 

  5. Jang, B., Kim, M., Harerimana, G., Kang, S.U., Kim, J.W.: Bi-LSTM model to increase accuracy in text classification: combining Word2vec CNN and attention mechanism. Appl. Sci. 10(17), 5841 (2020)

    Article  Google Scholar 

  6. Rehman, A.U., Malik, A.K., Raza, B., Ali, W.: A hybrid CNN-LSTM model for improving accuracy of movie reviews sentiment analysis. Multimedia Tools Appl. 78(18), 26597–26613 (2019)

    Article  Google Scholar 

  7. Mesnil, G., Mikolov, T., Ranzato, M.A., Bengio, Y.: Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv preprint arXiv:1412.5335, 17 December 2014

  8. Yin, R., Li, P., Wang, B.: Sentiment lexical-augmented convolutional neural networks for sentiment analysis. In: 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), June 26 2017, pp. 630–635. IEEE (2017)

    Google Scholar 

  9. Dhande, L.L., Patnaik, G.K.: Analyzing sentiment of movie review data using Naive Bayes neural classifier. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 3(4), 313–320 (2014)

    Google Scholar 

  10. Govindarajan, M.: Sentiment analysis of movie reviews using hybrid method of naive bayes and genetic algorithm. Int. J. Adv. Comput. Res. 3(4), 139 (2013)

    Google Scholar 

  11. Baid, P., Gupta, A., Chaplot, N.: Sentiment analysis of movie reviews using machine learning techniques. Int. J. Comput. Appl. 179(7), 45–49 (2017)

    Google Scholar 

  12. Samat, N.A., Salleh, M.N., Ali, H.: The comparison of pooling functions in convolutional neural network for sentiment analysis task. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds.) SCDM 2020. AISC, vol. 978, pp. 202–210. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36056-6_20

  13. Brar, G.S., Sharma, A.: Sentiment analysis of movie review using supervised machine learning techniques. Int. J. Appl. Eng. Res. 13(16), 12788–12791 (2018)

    Google Scholar 

  14. Mitra, A.: Sentiment analysis using machine learning approaches (lexicon based on movie review dataset). J. Ubiquitous Comput. Commun. Technol. (UCCT) 2(03), 145–152 (2020)

    Google Scholar 

  15. Lei, Z., Yang, Y., Yang, M.: SAAN: a sentiment-aware attention network for sentiment analysis. In: The 41st International ACM SIGIR Conference on Research & Development in Information, 27 June 2018, pp. 1197–1200 (2018)

    Google Scholar 

  16. Nezhad, Z.B., Deihimi, M.A.: A combined deep learning model for Persian sentiment analysis. IIUM Eng. J. 20(1), 129–139 (2019)

    Article  Google Scholar 

  17. Li, B., Liu, T., Du, X., Zhang, D., Zhao, Z.: Learning document embeddings by predicting n-grams for sentiment classification of long movie reviews. arXiv preprint arXiv:1512.08183, 27 December 2015

  18. Ray, P., Chakrabarti, A.: A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Appl. Comput. Inform. (2020)

    Google Scholar 

  19. Kane, B., et al.: ICAART, no. 1, pp. 498–505 (2021)

    Google Scholar 

  20. Ain, Q.T., et al.: Sentiment analysis using deep learning techniques: a review. Int. J. Adv. Comput. Sci. Appl. 8(6), 424 (2017)

    Google Scholar 

  21. Maulana, R., Rahayuningsih, P.A., Irmayani, W., Saputra, D., Jayanti, W.E.: Improved accuracy of sentiment analysis movie review using support vector machine based information gain. J. Phys. Conf. Ser. 1641(1), 012060 (2020)

    Article  Google Scholar 

  22. Gupta, C., Chawla, G., Rawlley, K., Bisht, K., Sharma, M.: Senti_ALSTM: sentiment analysis of movie reviews using attention-based-LSTM. In: Abraham, A., Castillo, O., Virmani, D. (eds.) Proceedings of 3rd International Conference on Computing Informatics and Networks. LNNS, vol. 167, pp. 211–219. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9712-1_18

    Chapter  Google Scholar 

  23. Dashtipour, K., Gogate, M., Adeel, A., Larijani, H., Hussain, A.: Sentiment analysis of Persian movie reviews using deep learning. Entropy 23(5), 596 (2021)

    Article  Google Scholar 

  24. Shen, Q., Wang, Z., Sun, Y.: Sentiment analysis of movie reviews based on CNN-BLSTM. In: Shi, Z., Goertzel, B., Feng, J. (eds.) ICIS 2017. IAICT, vol. 510, pp. 164–171. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68121-4_17

    Chapter  Google Scholar 

  25. Van, V.D., Thai, T., Nghiem, M.Q.: Combining convolution and recursive neural networks for sentiment analysis. In: Proceedings of the Eighth International Symposium on Information and Communication Technology, 7 December 2017, pp. 151–158 (2017)

    Google Scholar 

  26. Minaee, S., Azimi, E., Abdolrashidi, A.: Deep-sentiment: sentiment analysis using ensemble of CNN and Bi-LSTM models. arXiv preprint arXiv:1904.04206, 8 April 2019

  27. Kaur, H.: Sentiment analysis of user review text through CNN and LSTM methods. PalArch’s J. Archaeol. Egypt/Egyptology 17(12), 290–306 (2020)

    Google Scholar 

  28. Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: Sentiment analysis of comment texts based on BiLSTM. IEEE Access 9(7), 51522–51532 (2019)

    Article  Google Scholar 

  29. Haque, M.R., Lima, S.A., Mishu, S.Z.: Performance analysis of different neural networks for sentiment analysis on IMDb movie reviews. In: 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), 26 December 2019, pp. 161–164. IEEE (2019)

    Google Scholar 

  30. Jnoub, N., Al Machot, F., Klas, W.: A domain-independent classification model for sentiment analysis using neural models. Appl. Sci. 10(18), 6221 (2020)

    Article  Google Scholar 

  31. Cai, G., Xia, B.: Convolutional neural networks for multimedia sentiment analysis. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC 2015. LNCS (LNAI), vol. 9362, pp. 159–167. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25207-0_14

    Chapter  Google Scholar 

  32. Li, W., Zhu, L., Shi, Y., Guo, K., Cambria, E.: User reviews: sentiment analysis using lexicon integrated two-channel CNN–LSTM family models. Appl. Soft Comput. 94, 106435 (2020)

    Article  Google Scholar 

  33. Thet, T.T., Na, J.C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 823–848 (2010)

    Article  Google Scholar 

  34. Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I.: Twitter sentiment analysis using deep convolutional neural network. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 726–737. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_60

    Chapter  Google Scholar 

  35. Dang, C.N., Moreno-García, M.N., De la Prieta, F.: Hybrid deep learning models for sentiment analysis. Complexity 2021 (2021)

    Google Scholar 

  36. Bhatt, S., Jain, A., Dev, A.: Monophone-based connected word Hindi speech recognition improvement. Sādhanā 46(2), 1–17 (2021). https://doi.org/10.1007/s12046-021-01614-3

    Article  Google Scholar 

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Arushi Garg, Soumya Vats, Garima Jaiswal & Arun Sharma

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  1. Arushi Garg
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  2. Soumya Vats
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  3. Garima Jaiswal
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  4. Arun Sharma
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Editors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Amita Dev

  2. Kamrah Institute of Information Technology, Gurgaon, India

    S. S. Agrawal

  3. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Arun Sharma

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Garg, A., Vats, S., Jaiswal, G., Sharma, A. (2022). Analytical Approach for Sentiment Analysis of Movie Reviews Using CNN and LSTM. In: Dev, A., Agrawal, S.S., Sharma, A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-95711-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95710-0

  • Online ISBN: 978-3-030-95711-7

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