Original scientific paper
https://doi.org/10.32985/ijeces.17.1.2
Sentivolve: Utilizing FastText, CRF, HAN, and Random Forests for Enhanced Sentiment Analysis
T. Anilsagar
; Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India
*
S. Syed Abdul Syed
; Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India
* Corresponding author.
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.
Keywords
Sentiment Analysis; FastText Embeddings; Conditional Random Fields; Hierarchical Attention Networks; Random Forest;
Hrčak ID:
342319
URI
Publication date:
5.1.2026.
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