90% Accuracy: The Power of Fine-Tuning Models in Sentiment Analysis

Aspect-Based Sentiment Analysis involves fine-tuning models like BERT to identify sentiment-expressing n-grams. These n-grams can be labeled as positive, negative, or neutral, though labeling is optional. A practical example can be found in a GitHub repository showcasing DeBERTaV3’s application on a sample dataset. Modern approaches include using instruction-tuned cloud-based LLMs, which can mark aspect n-grams in JSON format. The choice between cloud and local computing depends on available hardware resources. Rule-based methods using dictionaries are hardware-friendly but labor-intensive and less effective. Local fine-tuning of models like BERT or DeBERTaV3 requires a decent GPU and a dataset, yielding high-quality results. Tools like the transformers library or wrappers such as simpletransformers and FlairNLP simplify this process. Cloud-based models like ChatGPT-o1 and DeepSeek-R1 offer comparable or superior results but at a cost and with data privacy concerns. The second task, clustering aspects into categories, can be integrated or done separately, using additional models or semantic embedding clustering.

Source: stackoverflow.com

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