Product Dynamics & overall Sentiment Analysis

[pycsr]

DoD:

• generate the sentiment for each review
• create a word cloud
• unearth the major points that customer is talking about
• aggregate the average average, count of positive and negative reviews monthly, quarterly and annually.
• Once these are done see if can have comparison with competitor also look at the things at brand, category and sku level

[pycsr]

My findings about dataset:

• Amazon review has only label followed by product review, however it doesnt tak which product review is being given. It needs to inferred using wordcloud or some other technique. Ex: label2 Stuning even for the non-gamer: This sound track was beautiful! It paints the senery in your mind so well I would recomend it even to people who hate vid. game music! I have played the game Chrono Cross but out of all of the games I have ever played it has the best music! It backs away from crude keyboarding and takes a fresher step with grate guitars and soulful orchestras. It would impress anyone who cares to listen! ^_^
• Twitter dataset talks abput only domain specific not the exact product.
• Rsics data set also doesnt directly talk about any specific data, it is Q&A chats

Word cloud for 1 and 2 ratings from Amazon review: Rating_1

Rating_2