Robust Sentiment Analysis with Minimal Preprocessing: An Adversarial-Contrastive Approach

Pedapapu, Sairohith (2025) Robust Sentiment Analysis with Minimal Preprocessing: An Adversarial-Contrastive Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

Traditional sentiment analysis systems strongly rely upon detailed preprocessing of texts to handle noise, error, and informality in language, but such preprocessing removes potentially useful linguistic signals while increasing extra computation overhead. This work investigates whether one could merge adversarial training with contrastive learning to create robust systems that process minimally preprocessed texts. This introduced a unifying framework that unites Fast Gradient Sign Method (FGSM) adversarial training with InfoNCE contrastive learning and evaluated with three architectures (ELECTRA, BERT, LSTM) with both formal movie review texts and informal Twitter data. The cohesive framework features an enhanced noise injection system that evaluates nine distinct types of perturbations at intensities ranging from 0% to 80%. The findings indicate that performance correlates with both architecture and domain.

The LSTM architecture achieved significant improvements of 10.6% on movie reviews and 7.4% on Twitter data. The transformer architecture achieved modest improvements (BERT: 1.2%, ELECTRA: 2.2%) in formal text, but had inconsistent results on social media. The robust analysis indicates that adversarially trained models perform at 96.3% under harsh noise conditions, whereas baseline models achieve 94.2% performance. The results challenge notions of architectural obsolescence, illustrating that appropriate training methods can rejuvenate simpler approaches. The study provides practical recommendations for developing sentiment analysis systems that can handle spontaneously occurring text with minimal preprocessing, especially advantageous for resource-constrained situations requiring dependable performance with noisy user-generated information.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Chikkankod, Arjun UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2026 10:19
Last Modified:	26 Aug 2026 10:19
URI:	https://norma.ncirl.ie/id/eprint/9655

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