twitter sentiment analysis
TRANSCRIPT
![Page 1: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/1.jpg)
TWITTER SENTIMENT ANALYSIS
HARSHIT SANGHVI
![Page 2: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/2.jpg)
DATA COLLECTION AND PREPROCESSING
• 27 million tweets (180GB)
• Collected in a span of ~1 week (05/05/2015 to 05/09/2015)
• Using Java program running on Amazon EC2
• Stored into MongoDB on Amazon EC2
• Cleaning up text of the tweets• Punctuations, numbers, small words, remove stop words
• Filter tweets• In non-English language
• Without location data
![Page 3: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/3.jpg)
SENTIMENT ANALYSIS
• Create Sentiment Prediction model using• Opinion Lexicon (http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar)
• Using Movie Review Dataset (http://ai.stanford.edu/~amaas/data/sentiment)
![Page 4: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/4.jpg)
USING KNIME
![Page 5: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/5.jpg)
VISUALIZATIONS
![Page 6: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/6.jpg)
TWEETS PER DAY PER HOUR
![Page 7: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/7.jpg)
TOP 10 MOST USED HASHTAGS
• Shows most commonly discussed topic on twitter
![Page 8: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/8.jpg)
TOP 5 MOST POPULAR USERS
![Page 9: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/9.jpg)
WORD FREQUENCY
• Showing words with frequency > 500 and sorted Alphabetically
![Page 10: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/10.jpg)
WORD ASSOCIATIONS
• E.g. “Day” appears more with “Mother” and “Happy” and “Birthday”.
![Page 11: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/11.jpg)
LETTER FREQUENCY
![Page 12: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/12.jpg)
# OF WORDS BY LETTER FREQUENCY
![Page 13: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/13.jpg)
LETTER POSITION HEATMAP
![Page 14: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/14.jpg)
SENTIMENT TIMELINE
![Page 15: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/15.jpg)
PRESENTATION USING SHINY
![Page 16: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/16.jpg)
WORD CLOUD
![Page 17: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/17.jpg)
NEGATIVE TWEETS
![Page 18: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/18.jpg)
POSITIVE TWEETS
![Page 19: Twitter sentiment analysis](https://reader031.vdocuments.site/reader031/viewer/2022021812/58a324a51a28ab71398b5591/html5/thumbnails/19.jpg)
REFERENCES
• Opinion Lexicon (http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar)
• Using Movie Review Dataset (http://ai.stanford.edu/~amaas/data/sentiment)
• Twitter Data Mining & Visualizations (http://bit.ly/twtvis)
• R Studio (https://www.rstudio.com)
• Sentiment Analysis using KNIME (http://www.knime.org/blog/sentiment-analysis)