# TWSSenti: A Novel Hybrid Framework for Topic-Wise Sentiment Analysis on Social Media Using Transformer Models

arXiv: https://arxiv.org/abs/2504.09896 · HTML: https://mlsanity.com/paper/2504.09896/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 59 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis](https://mlsanity.com/paper/2406.00367/) (2024)
- [Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://mlsanity.com/paper/2407.09395/) (2024)

## Built on by (most cited first, 6 of 6)

- [Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation](https://mlsanity.com/paper/2510.02855/) (2025)
- [Comparison of Multiple Classifiers for Android Malware Detection with Emphasis on Feature Insights Using CICMalDroid 2020 Dataset](https://mlsanity.com/paper/2602.00058/) (2026)
- [spd-metrics-id: A Python Package for SPD-Aware Distance Metrics in Connectome Fingerprinting and Beyond](https://mlsanity.com/paper/2510.04438/) (2025)
- [AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining](https://mlsanity.com/paper/2510.16013/) (2025)
- [Entropy-Based Data Selection for Language Models](https://mlsanity.com/paper/2602.17465/) (2026)
- [Privacy Preserving Topic-wise Sentiment Analysis of the Iran Israel USA Conflict Using Federated Transformer Models](https://mlsanity.com/paper/2603.13655/) (2026)

## Abstract (arXiv)

Sentiment analysis is a crucial task in natural language processing (NLP) that enables the extraction of meaningful insights from textual data, particularly from dynamic platforms like Twitter and IMDB. This study explores a hybrid framework combining transformer-based models, specifically BERT, GPT-2, RoBERTa, XLNet, and DistilBERT, to improve sentiment classification accuracy and robustness. The framework addresses challenges such as noisy data, contextual ambiguity, and generalization across diverse datasets by leveraging the unique strengths of these models. BERT captures bidirectional context, GPT-2 enhances generative capabilities, RoBERTa optimizes contextual understanding with larger corpora and dynamic masking, XLNet models dependency through permutation-based learning, and DistilBERT offers efficiency with reduced computational overhead while maintaining high accuracy. We demonstrate text cleaning, tokenization, and feature extraction using Term Frequency Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), ensure high-quality input data for the models. The hybrid approach was evaluated on benchmark datasets Sentiment140 and IMDB, achieving superior accuracy rates of 94% and 95%, respectively, outperforming standalone models. The results validate the effectiveness of combining multiple transformer models in ensemble-like setups to address the limitations of individual architectures. This research highlights its applicability to real-world tasks such as social media monitoring, customer sentiment analysis, and public opinion tracking which offers a pathway for future advancements in hybrid NLP frameworks.