AI Sentiment Analysis Assignment: Comparative Study of Pre-trained Language Models

1. Introduction and Problem Statement

Advanced sentiment analysis is the one important component of NLP (Natural Language Processing) that makes it easier to analyze the viewpoints, feelings, and behaviors expressed in text. Conventional sentiment analysis techniques, such machine learning and lexicon-based models, frequently fail to identify sarcasm, context ambiguity, and a variety of language patterns. Transformer-based deep learning models, like BERT, RoBERTa, and GPT, have significantly improved sentiment categorization through the use of contextual embeddings and considerable pretraining. whereas each model has its limits, BERT and RoBERTa focus on bidirectional context but struggle with long-range dependency, whereas GPT excels at producing language that appears human but may lead to categorization errors (Archa Joshy and Sumod Sundar , March 2023).

This study compares the performance of BERT, RoBERTa, and GPT independently on sentiment classification tasks and develops a hybrid sentiment analysis model that leverages their complementary strengths. Regardless of these advancements, no single sentiment analysis framework effectively integrates the benefits of many approaches. By combining these algorithms, we hope to improve contextual comprehension, classify data more accurately, and generate sentimental explanations (Laxman B et al. 2025).

Hypothesis:
A hybrid approach combining BERT, RoBERTa, and GPT will outperform individual models in sentiment classification tasks by leveraging their complementary strengths in context understanding, accuracy, and explanation generation.

Research Questions:

1. What is the individual performance of BERT, RoBERTa, and GPT in sentiment analysis tasks?
2. What are each model’s strengths and weaknesses when it comes to sentiment classification?
3. Can a hybrid strategy that combines these models do better in sentiment analysis than using just one model?

2. Background and Significance:

In sentiment analysis, advanced machine and deep learning techniques have surpassed the rule-based method. Archa Joshy and Sumod Sundar, (March 2023) research about statistical techniques used in early models, but by using sequential dependency learning with deep learning advanced techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has potentially increased the accuracy for sentiment analysis. Transformers such as BERT, RoBERTa, and GPT revolutionized sentiment analysis by improving contextual grasp and generalization.

• BERT (Bidirectional Encoder Representations from Transformers): Processes text bidirectionally, understanding deep contextual meaning and improving classification accuracy (Prasanthi et al. 2023).
• RoBERTa (Robustly Optimized BERT Pretraining Approach): Builds upon BERT by optimizing pretraining methods, improving performance in subtle expressions. (Prasanthi et al. 2023)
• GPT (Generative Pre-trained Transformer): An autoregressive model with capability in contextual language generation, useful in sentiment interpretation and explanation (Laxman B et al. 2025).

Despite the fact that these developments, individual models still have drawbacks. Long-range dependencies are difficult for BERT and RoBERTa to handle, and GPT could produce erratic explanations. A hybrid approach seeks to address these problems by utilizing their complementary advantages (Archa Joshy and Sumod Sundar , March 2023).

3. Research Design and Methods:

Laxman B et al. (2025) and his team research work into the insightness of comparing GPT, RoBERTa, and BERT on standard sentiment analysis corpora such as Twitter Sentiment, IMDB, and Amazon Reviews. The following are the research methodology topics covered:

• Data collection and preprocessing: Text datasets will employ tokenization, removal of noise, and creation of embeddings.
• Model Training and Comparison: The accuracy, F1-score, and processing speed of each model after tuning for sentiment analysis tasks will be compared.
• Hybrid Model Integration: The models will be combined into a multi-tiered system. GPT will create explanatory insights, RoBERTa will fine-tune contextual nuance, and BERT will manage principal classification.
• Performance Comparison: To quantify the extent to which hybrid integration improves sentiment classification more effectively, performance statistically will be compared.
• Ethical Considerations: To guarantee impartiality and precision in sentiment analysis, the research will tackle possible biases in training data and investigate ways to mitigate them.

4. Applications Across AI Domains:

The proposed sentiment analysis software has wide-ranging applications, including:

• Social Media Monitoring: Detecting trends and analyzing public sentiment (Tina Babu et al . 2024).
• Customer Feedback Analysis: Enhancing decision-making by assessing customer opinions (Tina Babu et al. 2024).
• Patient Feedback Analysis: Understanding patient emotions through feedback analysis.

5. Expected Outcomes:

The proposed study will undertake an elaborate evaluation of the advantages and limitations of BERT, RoBERTa, and GPT for sentiment analysis. The study objective is to design an improved hybrid model that attains optimal contextual awareness and classification efficiency. Entrepreneurs, scholars, and politicians can implement the suggested tool for sentiment analysis across a wide range of sectors in practical use. Furthermore, the study will extend the frontiers of artificial intelligence by demonstrating the successful integration of transformer models in sentiment analysis. The study will also address the ethical issues of sentiment analysis, such as detection and mitigation of bias (Tina Babu et al. 2024).

6. Conclusion

This research will enhance sentiment analysis by evaluating and merging the current transformer models. By the advantages of BERT, RoBERTa, and GPT, the proposed system will provide high accuracy, deep contextual knowledge, and real-time sentiment analysis. The findings of the research will contribute to AI-based sentiment analysis and its applications in different domains.

7. Reference

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