Sentiment Analysis using a Multinomial LR-LSTM Model


  • Seema Rani, Jai Bhagwan, Sanjeev Kumar, Yogesh Chaba, Sunila Godara, Sumit Sindhu


Deep Learning, Sentiment Analysis, Machine Learning, LSTM, Classification


Sentiment analysis (SA) refers to a technique utilized to ascertain the emotional state conveyed in information or text. It involves categorizing the text into three classes: positive, negative, or neutral. For instance, when someone says "the aqi of the city is good," they are expressing a positive opinion about the aqi of a specific place, while the statement "the aqi is bad" reflects the opposite. The introduction of social media increased the amount of content on the internet of sentiment data. Users on various social media platforms have been able to offer their opinions on various products, services, etc. These opinions are often expressed on social media in the form of movie reviews, product reviews, user comments, posts, etc. In light of this context, one of the captivating research areas in Natural Language Processing (NLP) is Twitter sentiment analysis. The paper proposes a stacked Multinomial-LR-LSTM model for the classification of tweets into three classes. Tweets are re-annotated using Text Blob. Twitter Sentiment dataset was used for experiments with accuracy of 97%.


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Sindhu, S., Kumar, S., & Noliya, A. (2023, March). A Review on Sentiment Analysis using Machine Learning. In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA) (pp. 138-142). IEEE.

Khan, M. L., Ittefaq, M., Pantoja, Y. I. M., Raziq, M. M., & Malik, A. (2021). Public engagement model to analyze digital diplomacy on Twitter: A social media analytics framework. International Journal of Communication, 15, 29.

Hamed, A.R., Qiu, R. and Li, D., 2016. The importance of neutral class in sentiment analysis of Arabic tweets. Int. J. Comput. Sci. Inform. Technol, 8, pp.17-31.

Khan, L., Amjad, A., Afaq, K.M. and Chang, H.T., 2022. Deep sentiment analysis using CNN-LSTM architecture of English and Roman Urdu text shared in social media. Applied Sciences, 12(5), p.2694.

Li, G., Zheng, Q., Zhang, L., Guo, S. and Niu, L., 2020, November. Sentiment infomation based model for Chinese text sentiment analysis. In 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) (pp. 366-371). IEEE.

Naqvi, U., Majid, A. and Abbas, S.A., 2021. UTSA: Urdu text sentiment analysis using deep learning methods. IEEE Access, 9, pp.114085-114094.

Wang, Y., Huang, G., Li, J., Li, H., Zhou, Y. and Jiang, H., 2021. Refined global word embeddings based on sentiment concept for sentiment analysis. IEEE Access, 9, pp.37075-37085.

Wongkar, M. and Angdresey, A., 2019, October. Sentiment analysis using Naive Bayes Algorithm of the data crawler: Twitter. In 2019 Fourth International Conference on Informatics and Computing (ICIC) (pp. 1-5). IEEE.

Sehar, U., Kanwal, S., Dashtipur, K., Mir, U., Abbasi, U. and Khan, F., 2021. Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms. IEEE Access, 9, pp.153072-153082.

Tam, S., Said, R.B. and Tanriöver, Ö.Ö., 2021. A ConvBiLSTM deep learning model-based approach for Twitter sentiment classification. IEEE Access, 9, pp.41283-41293.

Amin, A., Hossain, I., Akther, A. and Alam, K.M., 2019, February. Bengali vader: A sentiment analysis approach using modified vader. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.

Davcheva, E., Adam, M. and Benlian, A., 2019. User dynamics in mental health forums–a sentiment analysis perspective.

Gaye, B., Zhang, D. and Wulamu, A., 2021. A Tweet sentiment classification approach using a hybrid stacked ensemble technique. Information, 12(9), p.374.

Twitter Sentiment Dataset: HUSSEIN, SHERIF (2021), “Twitter Sentiments Dataset”, Mendeley Data, V1, doi: 10.17632/z9zw7nt5h2.1

Loria, S., 2018. textblob Documentation. Release 0.15, 2(8).

Rupapara, V., Rustam, F., Shahzad, H.F., Mehmood, A., Ashraf, I. and Choi, G.S., 2021. Impact of SMOTE on imbalanced text features for toxic comments classification using RVVC model. IEEE Access, 9, pp.78621-78634.

Yu, B., 2008. An evaluation of text classification methods for literary study. Literary and Linguistic Computing, 23(3), pp.327-343.

Jiang, L., Cai, Z., Wang, D. and Jiang, S., 2007, August. Survey of improving k-nearest-neighbor for classification. In Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007) (Vol. 1, pp. 679-683). IEEE.

Kleinbaum, D.G.; Klein, M.; Pryor, E.R. Logistic Regression: A Self-Learning Text; Springer: New York, NY, USA, 2002.

Zhang, Y., Zhang, H., Cai, J. and Yang, B., 2014, May. A weighted voting classifier based on differential evolution. In Abstract and Applied Analysis (Vol. 2014). Hindawi.

Da Silva, N.F., Hruschka, E.R. and Hruschka Jr, E.R., 2014. Tweet sentiment analysis with classifier ensembles. Decision support systems, 66, pp.170-179.

Sherstinsky, A., 2020. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, p.132306.

Li, C., Wang, Z., Rao, M., Belkin, D., Song, W., Jiang, H., ... & Xia, Q. (2019). Long short-term memory networks in memristor crossbar arrays. Nature Machine Intelligence, 1(1), 49-57.

Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.

W. Medhat, A. Hassan, and H. Korashy, ‘‘Sentiment analysis algorithms and applications: A survey,’’ Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014, doi: 10.1016/j.asej.2014.04.011.

Lubis, A. R., Nasution, M. K., Sitompul, O. S., & Zamzami, E. M. (2021). The effect of the TF-IDF algorithm in times series in forecasting word on social media. Indones. J. Electr. Eng. Comput. Sci, 22(2), 976.




How to Cite

Seema Rani, Jai Bhagwan, Sanjeev Kumar, Yogesh Chaba, Sunila Godara, Sumit Sindhu. (2024). Sentiment Analysis using a Multinomial LR-LSTM Model. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 697–705. Retrieved from



Research Article