In the past few years, there has been a huge growth in the use of microblogging platforms such as Twitter. Spurred by that growth, companies and media organizations are increasingly seeking ways to mine Twitter for information about what people think and feel about their products and services. Companies such as Twitratr (twitrratr.com), tweetfeel (www.tweetfeel.com), and Social Mention (www.socialmention.com) are just a few who advertise Twitter sentiment analysis as one of their services. In this project we are using big data techniques to better do this analysis.

1. Sentiment Analysis on
Android Lollipop Using
Twitter API
Pedram Bashiri
Rohit Ruparel
Joseph Caprino

2. Introduction
• Huge growth in the use of microblogging platforms
such as Twitter
• Organizations mining Twitter for information
regarding what people think about their products
and services
• Twitratr (twitrratr.com), tweetfeel
(www.tweetfeel.com), and Social Mention
(www.socialmention.com) advertise sentiment
analysis

3. Overview
• Sentiment analysis on Google’s Android Lollipop
• We are using big data techniques to better do this
analysis
• Using Twitter API to download tweets
• Downloaded unique tweets from hashtags
#Android5 #AndroidLollipop as textiles

4. System Architecture

5. Achievements
• 30,000 unique tweets downloaded via Twitter4j
library interfacing with Twitter API
• Code checks for duplicate tweets and ensures
we have unique tweets
• Performed a word count analysis utilizing
MapReduce

6. Achievements
• Stored the results of the word counter utilizing
MapReduce into HBase
• Utilized HBase Java API
• Gathered the word count analysis data and
visualized it

7. Achievements
• Processed a number of tweets and determined
their sentiment
• Ignored “bad” tweets
• Utilized Stanford Core NLP for sentiment analysis
on remaining tweets
• Saved results of scores and their frequency
• Visualized the sentiment analysis results

8. Issues
• Sentiment Analysis:
• Ads without URLs captured
• Scores differ for almost identical tweets

9. Future Work
• Distributed sentiment analysis
• More advanced visualizations
• Creating our own dictionary to do sentiment
analysis

10. Demo