Abstract
The rise of technology in social media has resulted in an enormous quantity of textual ambiguity. Sentiment analysis provides a crux of subjective opinions stored in a large amount of text such that the data gets segregated into positive and negative. In this research two datasets are used i.e., Amazon Reviews and IMDb wherein, first, implemented machine learning models such as Naive Bayes, XGBoost, etc. for the sentiment analysis out of which Linear SVC performed the best for the IMDb dataset and Amazon Reviews dataset. Furthermore, implemented a deep learning model i.e., Bi-LSTM that outperformed machine learning models for both datasets. Next, implemented BERT, a pre-trained language model, showed better results than Bi-LSTM for both datasets. Lastly, proposed a hybrid CNN-LSTM model, wherein, for feature extraction CNN is used, while LSTM is used for classification. The proposed hybrid model has given the best ROC score for both datasets among all the models used in this paper.
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Riya, Rai, S., Rupal, Rani, R., Niranjan, V., Sharma, A. (2023). An Ensemble and Deep Neural Network Based Approaches for Automated Sentiment Analysis. In: Abawajy, J., Tavares, J., Kharb, L., Chahal, D., Nassif, A.B. (eds) Information, Communication and Computing Technology. ICICCT 2023. Communications in Computer and Information Science, vol 1841. Springer, Cham. https://doi.org/10.1007/978-3-031-43838-7_5
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