Global Analytics on Tweets Sentiment
(GATS)
Under the Guidance of
Dr. Sunanda Gupta
Prateek Sharma
2011ECS19
Rajesh Kumar Pathak
2011ECS42
Sunny Kumar
2011ECS43
Introduction- GATS
- Acquiring Data
- Storage of Data
- Cleaning Data
- Sentiment Analysis
- Visualization
First Phase - Acquiring Data
Second Phase:Understanding and Cleaning tweets
Third Phase:Acquiring Geo-location and country-codes
Fourth Phase:
Prepare Data for processing
Fifth Phase:
Sentiment Analysis of Tweet
Naive Bayes is a simple model for classification. It is simple and works well on text categorization. We adopt multinomial Naive Bayes in our project. It assumes each feature is conditional independent to other features given the class.
Sixth Phase:
Visualization (Analytics)
1)Plotting each tweet
2)Density Plotting of Tweets
3) Choropleth Concept
4) Graphical Study
Technologies Used:
- Twitter Streaming API as a source of data.
- Python for manipulating the data.
- MongoDB as the database.
- TextBlob library that use Naive Bayes algorithm for processing the sentiments of the tweets.
- R for visualizing the data as dots on map, choropleth and through graphs
References
- https://github.com/sunnykrGupta/Glob_Analytics
- htmlwidgets.org/showcase_leaflet.html
- http://dev.twitter.com
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