Twitter sentiment analysis

This project aims to preprocess live tweets for further analysis. Steps involved:

• Raw data collecting
• Data preprocessing
• Demographics prediction
• Category prediction
• Sentiment prediction

Getting Started

Prerequisites

emoji==0.5.4
Keras==2.3.1
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
tensorflow==1.15.0
Shapely==1.6.4.post1
torch>=1.0.0
numpy>=1.13
tqdm
Pillow
torchvision>=0.2.2
pycld2>=0.31
requests
pandas>=0.20

Also you need tweeter application access keys that you define in listener.py You will need (consumer_key, consumer_secret, access_token, access_token_secret) Link

Installing

• m3inference package pip install git+https://github.com/SlavOK400/m3inference.git

• twitter_sentiment package git lfs install then git lfs clone https://github.com/SlavOK400/twitter_sentiment.git

• Define your twitter app keys: consumer_key, consumer_secret, access_token, access_token_secret in listener.py

• Mongo database. Please install the latest stable version and run MongoDB server. Link

Usage

0. Go to twitter_sentiment directory on your machine
1. Run listener.py to start collecting tweets
2. Run mongoexport --db tweets --collection training_tweets --out *.json, where * is the name of your file, to convert collected tweets to a json file
3. Run infer_demographics.py *.json, where is the name of your file, to predict demographics, categories and sentiment for your tweets. Input: json files. Output: single _inferred.csv files

Originally all tweets from Canada will be collected, If you wish to change the location please update listener.py with:

if "place" in datajson and datajson["place"]['country_code'] == "US": and stream.filter(locations=LOCATIONS_US )

Authors

• Slava Spirin
• Winston Li

License

This project is licensed under the MIT License - see the LICENSE.md file for details