Novel Model for the Sentimental Analysis of Twitter Data Using Deep Learning
DOI:
https://doi.org/10.54536/ajicti.v1i1.6699Keywords:
Kaggle, LSTM, Machine Learning, Naive Bayes, Sentiment Analysis, TwitterAbstract
Sentiment Analysis is the scientific technique of studying various tweet data from Twitter, hence specifying them either positive, negative, or a neutral one. Numerous algorithms/techniques exists for sentiment analysis like “KNN”, “Naïve Bayes” and “Random Forest” which are based on machine learning or deep learning. Although, such models are employed with an accuracy much less than that required for a better and novel framework model. Therefore, it is very necessary to develop a light-weight, accurate and robust model with high precision and accuracy. Through this study, we have suggested an efficient and novel framework for sentiment analysis of Twitter data named as Improved LSTM Model. We modified/variated the base network of LSTM employing additional layers for better accuracy. We employed an improved LSTM technique for sentiment analysis in our improved model that categorizes the Twitter data better by classifying it into positive, negative, and neutral sentiments. Moreover, the proposed model is more compact due to its novel architecture than the base network. Furthermore, the performance of the traditional/old machine learning models named Random Forest, KNN, and Naïve Bayes based on Twitter data have been analyzed, however, the performance and results of our proposed DL model are much better than ML algorithms. We employed Kaggle dataset for the training/testing of the proposed model, which contains almost 1 million tweets. To evaluate the efficiency of the improved model, we utilized extensive experimentation which demonstrates that our algorithm beats existing sentiment analysis approaches, with an average accuracy of 90.
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Copyright (c) 2026 Afrasiab Khan, Rabbia Mahum, Md Hamza (Author)
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