Sentivolve: Utilizing FastText, CRF, HAN, and Random Forests for Enhanced Sentiment Analysis

Abstract

The objective of this study is to enhance sentiment analysis through an integrative approach termed Sentivolve, which combines FastText embeddings, Conditional Random Fields (CRF), Hierarchical Attention Networks (HAN), and Random Forests (RF). The system aims to improve sentiment classification by leveraging advanced feature extraction, sequence modeling, attention mechanisms, and ensemble learning. FastText captures subword information for better text representation; CRF models sequential dependencies; HAN highlights key textual elements using a hierarchical attention structure; and Random Forests aggregate predictions to ensure consistent sentiment classification. Experimental results demonstrate that Sentivolve outperforms traditional models in both accuracy and generalizability. This integrated approach provides an effective solution for sentiment analysis, especially in handling diverse and complex text data.