Author(s): N John Kuotsu

Abstract: Sentiment analysis (or opinion mining), is a natural language processing (NLP) technique that harnesses Information from text data that is naturally subjective. Opinions, emotions, and attitudes are things that this information can contain. In recent years, there has been an increasing focus on sentiment analysis because of the explosive growth of unstructured data on the internet. Sentiment analysis tasks have seen state of the art performance using deep learning models. However, these models can be computationally intensive and high dimensional can produce problems of overfitting when working with relatively limited data. In order to overcome these limitations, a new hybrid model is proposed using a combination of LSTM network for feature extraction and Random Forest classifier for the final sentiment classification improving the accuracy of the sentiment analysis, while reducing computational cost. In this paper, the proposed hybrid model is designed and implemented, and evaluated on a publicly available dataset of movie reviews.

DOI: 10.33545/26633582.2025.v7.i1a.153

Pages: 32-39 | Views: 458 | Downloads: 206

Download Full Article: Click Here