Hey everyone! I'm developing an app that needs to analyze texts to find negative emotions and pain points. Currently, I'm using a HuggingFace model to classify texts based on emotion, but I've noticed that these models tend to perform better on short sentences rather than larger paragraphs. I'm looking for advice on whether I should stick with this text classification model or consider using AI, like ChatGPT, to help filter and detect emotional content from a larger dataset—around 1000 texts. Is it feasible for AI to process and return an array of texts that display pain points, especially anger or frustration? Any insights would be greatly appreciated!
2 Answers
If you're assessing whether long paragraphs are positive, neutral, or negative, traditional models might be the way to go—plus, they’re much more cost-effective than AI models. However, if your goal is to extract specific complaint points, LLMs could yield better quality output. Since you're dealing with a relatively small amount of data (only a thousand texts), investing too much time in complex systems might not be worth it. You've got options!
Using traditional NLP for filtering or sentiment analysis can be more reliable and straightforward, especially since they’re easier to adjust if something goes wrong. On the flip side, LLMs like ChatGPT are powerful but can produce inaccurate results sometimes. If you're aiming for high reliability, you could try using both methods together and only trust results when they align.
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