The objective of this repository is to create prediction on product ratings based on a text given from customer feedback. This repository will be creating a model to predict the rating of product 1-5 stars with 5 being the highest.

To create the model I will be training the model on Amazon reviews. Amazon product reviews have a star rating system so that can be used as the target output prediction.

There are different methods for classifying text as a rating system.

Learns by the joint probability distribution p(x,y) then by using Bayes is transformed to p(y/x) however you are not directly computing p(y/x). In this classifier for text analysis, there is an assumption that the words are independent of one another.

Learns the conditional probability distribution p(y/x) "probability of y given x. This type of model would read the data sequentially to try and figure out the difference in ratings based on examples. In this experiment I tried a number of different models (CNN, Multi Size CNN, and a LSTM).

The input variable in this is the text summary of a review, and the target output is the rating

• The target variable (rating) I converted this to one hot encodings
• Vectorized the text summary, I also removed emojis text
• Calculated the average, max, min of the text summary
• Set a sequence length that is appropriate
• Tested x3 models (single hidden layer, CNN, LSTM)

Out of the three models, I tested. The LSTM model had an accuracy of 0.898. It should be noted minimal fine-tuning of the model was done and the model was only trained on 10,000 of the 500,000 available rows.