Sentiment analysis: a comparison of deep learning neural network algorithms with ensemble learning algorithms

Abstract

Sentiment analysis presents difficulties, especially in multi-language contexts, when dealing with the meanings based on context, mockery, and complex language. In contemporary NLP applications, the exact classification is necessary for researchers and institutions to obtain valuable visions and increase customer participation and improve decisions. Our method improves the performance of Sentiment analysis by using many advanced deep learning models, such as XLNET, Roberta and Bert. In order to capture a variety of language patterns and increase classification accuracy, we plan to integrate these models using ensemble techniques such as stacking, bagging, and boosting. We applied this approach to the dataset [cardiffnlp/tweetsentimentmultilingual], ensuring a comprehensive evaluation of each model performance in addition to the effectiveness of the group as a whole. The results show that our collection method works better than independent models, where you get F1 degrees and higher accuracy in various sentiment classes. Mixing predictions from various models often uncovers surprising insights. In most cases, ensemble learning really shines when tackling today’s NLP challenges and reliably nails sentiment classification even if things sometimes get a bit messy. We took a deep dive into a range of neural network models designed for sentiment analysis, checking out both their solo performance and what happens when they team up. Stacking, bagging, and boosting were thrown into the mix to craft a fresh, hybrid method that bumps up accuracy noticeably. Compared to models running on their own, our approach usually scores higher on the key measures, proving that joining forces is a pretty solid trick for sorting sentiments.