My objective is to classify objects that all belong to a certain category, based on a textual description of these objects by humans. My problem is not specific to a certain category of objects, but for sake of clarity I am going to give examples as if the objects I wanted to classify were movies.
To be precise:
- the description contains both a judgement of the object, and a more objective description of the various parts of the object. For example: “This movie has great lines, and the scenario is well-though. It counterbalances the poor actor performance. Still, overall I think it's a very good movie”. This both contains information about different aspects of the movie, and provides a subjective review.
- what I want is:
- a score for each object (like a movie rating), based on how appreciated it is;
- for a given object, "similar" objects (ie. if you liked this movie, you might also enjoy these), based on similar "features" each object has. For instance, a movie which was also well-written might be considered "similar" to the former example.
- I also have access to a pre-existing classification of these objects. For instance, a movie might be labeled "action/thriller". This classification is too broad for my purposes (ie. not all "action/thriller" movies are similar), but it might be a good start.
I have though that to solve my problem, I could use sentiment analysis to give each object a score, and that natural language processing coupled with a feature space could do the trick for classifying objects.
The point is that I am unsure on how to proceed, because I am new to machine learning, natural language processing, and data sciences in general. I have nonetheless a CS and mathematical background.
Could you provide some insight on where to start? Are there libraries that already provide this kind of features?
This is a repost of this question, since it was not focused enough, and this forum seems more appropriate. It has been rewritten.