Deep learning model for semantic classifier

[Shashankwer]

Just a thought.

Based on the amazon data-set extracted from Amazon, we will have the following data:

X : set of words representing the review by the user
Y: Rating given to the particular product

If we can built a deployable model based on the input text give sentiment/review of the user.

An encoder-decoder model can be thought of where encoder can encode the input the text and decoder will simply predicting the output(no attention hopefully required).

Benefits:

1. If implementable, then the model will be robust of the length of the text.

Issues:

1. Data has a review text of different length. (1 word simply good or bad) to large text.
2. The model can further be improved to give a prediction of the topic the sentiment is about (about a feature/service/product classes). Input here needs to be constructed for the model.
3. The model can have issues with the review containing many sentences some describing good features some describing why some features were bad. Rating given are generally generalised. On the positive side if we can segment it in form of sentences/paragraphs then a prediction can be given for each sentence. and weighted review would be the final review for the same. In such scenarios Issues with mixed sentiment can be resolved better by the system.
4. Model depends on the accuracy :( needing to be trained.

Request to let me know your comments on the same.