Emotion Detection And Classification of Tweets

How to Run

• Go to your terminal.

• Clone this repository and go inside it

   git clone https://github.com/mjaglan/PyTextSentiment.git
   cd PyTextSentiment
  

• Edit following files:

   app/assets/BingCredentials/bingClientId.txt
   app/assets/BingCredentials/bingClientSecret.txt
   app/assets/TwitterAPI/credentials.txt
   app/assets/input.txt
  

• Run the following script

   . ./restart-all.sh
  

Web UI

• This application inside docker container page can be browsed at http://CONTAINER-IP:9091/

Project Structure

• Training dataset files: app/assets/Resource/searchKeys
• Get Feeds by text search query: app/assets/Resource/searchKeys/testFiles
• Bag of Words for emotion tagging and classification: app/assets/Resource
• Output twitter data directory: app/assets/twitterData

Project Overview

The term paper of this work is present here. Below are the highlights of the work done + the results generated for live tweets on 28 December 2015.

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References

• Bing Package
• TwitterAPI Package and TwitterAPI end point requests
• Google Geo Package
• Scikit-learn Package
• Twitter Application Management
• Stream API Request Parameters