Catwigopy is a tool which use the power of Natural Language Processing to provide an easy way to generate a preferences profile for a given Twitter user.
Here are all the possible categories to classify a user:
- Music and Radio
- Movies
- Videogames
- Books and Literature
- Careers
- Personal Health
- Religion and Spirituality
- Home and Garden
- Personal Finance
- Pets and Animals
- Shopping
- Travel
- Automotive
- Fine Art
- Science
- News and Politics
- Education
- Food and Drink
- Financial and Business
- Family and Relationships
- Style and Fashion
- Medical Health
- Software and Technology
- Events
- Hobbies and Pop Culture
- Sports
To install the latest version of catwigopy from PyPi use:
pip install catwigopy
All the dependencies are already listed on the setup file of the package, but to sum them up, you will need the following requirements:
IMPORTANT
To use Catwigopy you need to create a Twitter application, in order to get the acces tokens and connect with the Twitter API service.
First of all, import the module
from catwigopy import Catwigopy
This next line, creates an instance of Catwigopy for the specified user username. It also retrieves user's tweets. By default, number_of_tweets is 1200. You can decrease the value to get better execution times, but the prediction accuracy will be penalized. You will need to provide the credentials fot the Twitter API.
new_user = Catwigopy('Tesla', consumer_key, consumer_secret, access_token, access_token_secret, number_of_tweets=1200)
Now you can display the user's name, username, image and description.
new_user.get_user_name()
new_user.get_user_username()
new_user.get_user_image()
new_user.get_user_description()
You can also retrieve the hashtags terms count, the tweet terms count and the topics terms count:
res = new_user.get_hashtags_terms_count()
res = new_user.get_tweets_terms_count()
res = new_user.get_topics_top_terms(nterms=30)
To retrieve the user timeline:
new_user.search_user_timeline(number_of_tweets)
Now, classify the tweets with the model and get the results in a dictionary with:
res = new_user.get_user_classification()
Here is the result of a simple Catwigopy use. Here I only expose the top 3 categories for each user, but all them are returned in the dictionary.
| Username | First Category | Second Category | Third Category |
|---|---|---|---|
| Tesla | Automotive (70.92%) | Software and Technology (10.07%) | Travel (3.86%) |
| realDonaldTrump | News and Politics (28.02%) | Financial and Business (11.36%) | Sports (8.02%) |
| sciencemagazine | Science (24.41%) | Medical Health (16.73%) | Education (12.03%) |
| NintendoAmerica | Videogames (41.65%) | Hobbies and Pop Culture (12.19%) | Sports (9.74%) |
Please, bear in mind that Catwigopy has been developed only for research purpose. Make sure that you don't store the results obtained with Catwigopy and, in any case, don't use this results with comercial purposes.
Catwigopy efficiency and accuracy have been tested with enterprise accounts like Nintendo or Tesla, and also with public figures accounts, like Theresa May or Pontifex. If you want to analyse personal accounts, even if them are public, make sure you have the explicit consent.