A group project done for a web mining class. Amazon movie reviews, published by Jure Leskovec. Assistant Professor of Computer Science at Stanford University on his personal site. Preparing Dataset: 1- Wrote a parser to convert txt file into CSV using R Compiler 2- Developed a NodeJS middleware to gather information about movie Model selection & optimization: 1- Calculated basic sentiment score for each review 2- Created a word cloud for each movie by combining all reviews 3- Calculated Pointwise Mutual Information (PMI) score for each movie 4- Aggregated all the scores to get an overall movie score Value: If amazon adds the overall aggr. score and wordcloud, alongside with its general rating for each product. Then it will save users a lot of time from reading through all the reviews and they can easily picture the overall user sentiments regarding that product.

1. SENTIMENT ANALYSIS
AMAZON MOVIE REVIEW DATASET
IS 688 – WEB MINING
INSTRUCTOR: CHRISTOPHER MARKSON
TEAM MEMBERS: Maham | Amit | Mashael | Karan | Nidhish

2. OUTLINE
• Data Source, Collection & Parsing
• Model Selection & Optimizing Parameters
• Methods / Code Sample
• Results Overview & Value

3. DATA SOURCE, COLLECTION & PARSING
Amazon movie reviews, published
by Jure Leskovec. Assistant
Professor of Computer
Science at Stanford University on
his personal site.

4. PROBLEMS
• Format was not R-Friendly
• Only partial information was available, data context were missing
• we had reviews but no information about the movie

5. WORKAROUND / SOLUTION
• Wrote a parser to convert JSON txt file into CSV using R Compiler
• Developed a NodeJS middleware to gather information about movie

6. PREPARED FILES
After parsing, and gather more data using Amazon Web Service, we got following 2 files
&
Reviews
Movie Details

7. MODEL SELECTION & OPTIMIZATION
• Basic Sentiment Score for Each Review, using Syuzhet package
• Provides 4 types of method, bing, afinn, nrc, Stanford; AFFIN has weighted 2477 words and phrases
• Uses coreNLP, stringr libraries mainly.. Emotional trajectory of review
• Create WordCloud for Each Movie, using wordcloud package
• Combined all reviews into one variable, calculated term frequency & generated WordCloud images
• Used tm (text minig), SnowballC (text stemming), RColorBrewer (color palettes) alongside
• Pointwise Mutual Information (PMI) Sentiment Score for Each Movie, using RCurl package
• Wrote our own function
• Movie_Title vs Excellent/Poor, Movie_Genre vs Excellent/Poor
• Final score was the ratio of Movie_Title / Movie_Genre

8. MODEL SELECTION & OPTIMIZATION
• Aggregated all the Sentiment Scores
• Took Median of all the users review score
• Took Median of all the users review text sentiment score
• Assigned an overall Sentiment Score to each movie
• Took median of
• User Review Score Aggr,
• User Review Text Sentiment Score Aggr,
• Movie_Title vs Genre PMI Score

9. METHODS / CODE SAMPLE
Basic Sentiment Score
WordCloud

10. METHODS / CODE SAMPLE
Aggregation
PMI

11. RESULT OVERVIEW & VALUE

12. RESULT OVERVIEW & VALUE
The Count of Monte Cristo [Region 2]
Far from Home
Phonics Volume 1

13. RESULT OVERVIEW & VALUE
• Alongside with aggregate user reviews, Amazon can present
• overall rating score, and
• Word Cloud local to that product
• This will save users a lot of time to read through all the reviews and they can easily picture the overall
user sentiments regarding that product.

14. THANK YOU