I previously asked a question about categorizing sentiment (level of happiness) in sentences using Machine Learning and some user proposed using an ordinal regression to solve this problem. In the question, I stated that my training and my test data might include "neutral" or "N/A" sentences. For example:
- Positive sentiment: "Today is the best day of my life", "I am feeling good"
- Neutral or N/A sentiment: "I am doing okay", "My dog is 7 years old"
- Negative sentiment: "I hate my life and want to disappear", "I am a little sad"
I would like to know how I should categorize the sentences in my training data.
Should I use
1 for negative sentences,
2 for neutral and N/A sentences and
3 for positive sentences or is it possible to use
-1 for negative sentences,
0 for neutral and N/A sentences and
1 for positive sentences. I have never used ordinal regression in the past and I am wondering if it is even possible to use negative or zero-valued groups/categories.
The latest option, if possible, might produce more "intuitive" predictions. For example, a prediction of
0.55 would indicated a generally positive sentiment and
-0.2 might indicate a slightly negative sentiment.