Shubxam
commented
1 month ago solution by Claude:
To address the issue of determining the magnitude of negativity in addition to the probability, you can consider the following approaches:
Use a multi-class classification model: Instead of binary classification (positive/negative), train a model to predict multiple classes representing different levels of sentiment (e.g., very negative, somewhat negative, neutral, somewhat positive, very positive). This would provide more granular sentiment information.
Implement a regression model: Train a model to predict a continuous sentiment score (e.g., from -1 to 1) rather than discrete classes. This would directly give you both the direction and magnitude of sentiment.
Calibrate probabilities: While not ideal, you could attempt to interpret the probability as a rough indicator of magnitude. However, this approach has limitations and may not be reliable.
Ensemble methods: Combine multiple models or approaches to get a more nuanced view of sentiment, including both classification and regression models.
Use lexicon-based methods alongside the model: Incorporate rule-based sentiment analysis using sentiment lexicons to supplement the model's output with more detailed sentiment information.
Fine-tune on a dataset with magnitude labels: If possible, fine-tune your model on a dataset that includes sentiment magnitude labels, allowing it to learn to predict both sentiment direction and intensity.
To implement these solutions, you'd likely need to retrain or fine-tune your model, or incorporate additional models/techniques into your pipeline. The best approach depends on your specific requirements, available data, and resources.
Would you like me to elaborate on any of these approaches or discuss how you might implement one of them in your current setup?
current "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis" model, gives us a sentiment score for a headline.
in this example, the score of 0.99 tells us about the probability of the sentiment being negative, but doesn't tell us the magnitude of negativity i.e. how more of less negative the sentiment is. How to fix that?