Abstract
This study investigates the use of machine learning and natural language processing techniques for text sentiment analysis. The study collected a dataset of over 10,000 online reviews, and applied pre-processing, feature extraction, and model training techniques to predict the sentiments of the reviews. The results indicate that the trained models achieved high levels of performance, with an average accuracy, precision, recall, and F1-score of 0.87, 0.88, 0.87, and 0.87, respectively. The study also conducted a human-based evaluation and mixed-effect analysis to further assess the performance of the models. The results demonstrate the effectiveness of the proposed approach for text sentiment analysis, and suggest promising directions for future research.
Citations
IRE Journals:
Priyanshu Modi , Atul Kumar
"Getting Behind the Sentiments - A Review of Text Sentiment Analysis Methods" Iconic Research And Engineering Journals Volume 6 Issue 7 2023 Page 314-319
IEEE:
Priyanshu Modi , Atul Kumar
"Getting Behind the Sentiments - A Review of Text Sentiment Analysis Methods" Iconic Research And Engineering Journals, 6(7)
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