Raw code written and executed at Google Colab : FOR END-EVALUATION : https://colab.research.google.com/drive/1MVInaCIV1e6-wl8eQYNFZxDdpwdni7Tz#scrollTo=4I1UOMg9Lpo7 SECONDARY GOALS : https://colab.research.google.com/drive/1rZsGGzT0ODys4J72z4R8XwwISMivBYq_#scrollTo=8Uwn4Lte_eEM I have finished the code for LDA model, trained the model, tuned the model, calculated the Coherence Score ( it came out to be 0.39888 compared to the optimal value of 0.454) and also I finished the part of secondary goal which was the sentiment analysis of the reviews. I have created Flask Interface for Secondary goals part but it might not display any rating beacuse of some bug & I could not correct it as of the time of submission but I will surely fix the bug asap & will merge the two files to create a final model which can show both the proportion of words in the review as well as the nature of the review( +ve/-ve ). My Flask API Link : http://127.0.0.1:5000 I have attached all the files I created & used.

How to interpret the LDA model ? The given image shows the output of an LDA model which shows the AVERAGE TOPIC COHERENCE along with the tokens(words) with their proportions in the document we created in LDA.