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:

1.  Twitter Streaming API as a source of data.
2.  Python for manipulating the data. 
3.  MongoDB as the database.
4.  TextBlob library that use Naive Bayes            algorithm for processing the sentiments  of the tweets.
5. R for visualizing the data as dots on map, choropleth and through graphs

References

1. https://github.com/sunnykrGupta/Glob_Analytics
2. htmlwidgets.org/showcase_leaflet.html
3. http://dev.twitter.com

4. https://dev.twitter.com/docs/streaming-apis/parameters

5. http://www.mongodb.org/

6. http://api.mongodb.org/python/current/tutorial.html

7. https://maps.yandex.com

8. https://developers.google.com/maps

9. www.geonames.org