Evaluating Performance of Sentiment Analysis Models on Diverse Datasets

Published on 2025-11-12 • Avichala Research

Evaluating Performance of Sentiment Analysis Models on Diverse Datasets – Research Summary for Avichala

Abstract: This paper investigates the performance variability of several sentiment analysis models – including RoBERTa, Twibot, and a BotRGCN – across a range of datasets, emphasizing differences in their ability to accurately classify sentiment in diverse textual domains, specifically focusing on the impact of data diversity on model effectiveness. The results highlight significant discrepancies in performance, demonstrating the critical need for data-aware model selection and further emphasizing the limitations of ‘one-size-fits-all’ approaches in sentiment analysis.

Problem Statement: Sentiment analysis models have become increasingly prevalent in applications such as market research, brand monitoring, and political opinion tracking. However, current sentiment analysis systems often demonstrate limited robustness when deployed across diverse datasets, ranging from social media conversations to news articles. The research problem addressed here is determining the extent to which pre-trained models – like RoBERTa – generalize effectively when exposed to datasets with varying linguistic styles, topics, and levels of emotional intensity. The study seeks to quantify these differences and expose the vulnerabilities of relying solely on large-scale pre-training for all sentiment classification tasks. The motivation for this research lies in the practical need for more reliable and adaptable sentiment analysis systems, particularly in scenarios where data distributions are heterogeneous.

Methodology: The paper employs a comparative empirical approach, evaluating four models across a set of diverse datasets:

RoBERTa: A standard pre-trained language model, serving as a baseline.
Twibot: A smaller, dedicated chatbot model.
BotRGCN: A Graph Convolutional Network model, potentially offering advantages in capturing relational information within textual data.
Standardtr: A previously reported model, used for comparison and benchmarking.
Datasets: The models were tested on five datasets, categorized based on their domain and complexity: AMR+CIGA, Text-Level*, Sentiments, Values, and Topics. Data was split into training (1524-1556 samples) and test (764-382, 216, 1002, 4004, 864) sets. The metrics used were accuracy, precision, recall, and F1-score. Model-level performance was assessed, alongside dataset-level evaluations. The authors utilized standard evaluation protocols (training, test splits) to ensure fairness and comparability. The "Shortcutte Standardte" model provided a baseline for comparison, showing the initial gains achieved with RoBERTa. Additionally, the study investigated the impact of model parameters and training strategies (though not extensively described) on performance.

Findings & Results: The study revealed significant variations in sentiment analysis performance across the models and datasets. Several key findings emerged:

Dataset Dependency: RoBERTa consistently outperformed the other models on the Text-Level* and Sentiments datasets, demonstrating a strong reliance on the broader training data. However, it struggled more on the Topic and Values datasets, highlighting the influence of domain-specific language and values.
Model-Level vs. Dataset-Level: The model-level performance was substantially higher than the dataset-level, indicating an ability to leverage general knowledge learned during pre-training. The BotRGCN model achieved notably better performance on the Topic dataset, likely due to its ability to capture contextual relationships. The Standardtr model served as a key benchmark, and its performance significantly dropped on the Text-Level* dataset.
Significant Differences in F1-Scores: The biggest surprises came with the drastic differences in F1-scores across the datasets – particularly the 87% increase on the Sentiments dataset and the 349% increase on the AMR+CIGA dataset, suggesting substantial data-driven biases or differences in labeling.
Emotional Nuance: The model's ability to distinguish between nuanced emotions (anticipation, joy, etc.) was strongly correlated with the dataset’s complexity and linguistic diversity.

Limitations: The study's limitations include a relatively small number of datasets assessed, a lack of deep exploration of different model training strategies (optimization algorithms, learning rates), and a focus primarily on accuracy-based evaluation. The paper doesn’t offer extensive insights into the inner workings of the models themselves, particularly regarding the mechanisms underlying their success on certain datasets. Furthermore, the absence of a thorough bias analysis prevents a complete understanding of the factors driving the observed performance variations. Finally, the study doesn't address the scalability of these methods or explore methods for reducing the data dependency of these models.

Future Work & Outlook: Future research directions could explore the use of adversarial training to enhance model robustness, investigate methods for adapting pre-trained models to specific domains using few-shot learning, and develop more sophisticated bias detection and mitigation techniques. Integrating attention mechanisms could provide greater insight into the models' decision-making processes. Further research should examine methods for incorporating domain knowledge into the training process and exploring the use of multi-task learning to improve generalization. The exploration of unsupervised and self-supervised learning techniques for sentiment analysis holds significant potential. Finally, assessing the computational cost and resource requirements of these models in real-world deployments is essential.

Avichala Commentary: This paper underscores a critical issue in the rapidly evolving landscape of LLMs and AI Agents – the inherent fragility of models trained on broad datasets when deployed in specialized domains. It's a stark reminder that a ‘good’ general-purpose model isn’t automatically a ‘good’ sentiment analysis model. It reinforces the growing need for more data-aware model design, potentially leading to a shift toward hybrid approaches combining general pre-training with domain-specific fine-tuning and continual learning strategies. The findings highlight a fundamental challenge in building truly robust and adaptable AI agents—a challenge that will significantly shape the future of sentiment analysis and influence the development of more intelligent, context-aware systems. The paper serves as a valuable contribution to the broader discussion around LLM robustness, emphasizing the importance of rigorous evaluation across diverse data distributions.

Link to the Arxiv: https://arxiv.org/abs/2511.08455v1.pdf