https://github.com/irishmorales/modelling-personality-from-twts
ML model for personality trait recognition using structural metrics of Twitter tweets. Gold in Poster Presentation, DLSU Senior High Research Congress
https://github.com/irishmorales/modelling-personality-from-twts
big-five-traits machine-learning nlp nlp-machine-learning personality-predicting personality-traits twitter
Last synced: 2 months ago
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ML model for personality trait recognition using structural metrics of Twitter tweets. Gold in Poster Presentation, DLSU Senior High Research Congress
- Host: GitHub
- URL: https://github.com/irishmorales/modelling-personality-from-twts
- Owner: IrishMorales
- Created: 2020-07-13T22:05:01.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-30T09:15:46.000Z (over 1 year ago)
- Last Synced: 2025-01-27T06:30:23.566Z (4 months ago)
- Topics: big-five-traits, machine-learning, nlp, nlp-machine-learning, personality-predicting, personality-traits, twitter
- Language: Python
- Homepage:
- Size: 8.64 MB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# modelling-personality-from-twts
A study by nerds.
Okay, seriously. Scripts for personality trait recognition based on structural metrics of tweets.
Full paper: ["Analyzing Structural Metrics to Predict Twitter User Personality Traits"](https://www.academia.edu/43608598/Analyzing_Structural_Metrics_to_Predict_Twitter_User_Personality_Traits).
Made with @zivhd & @kildor22. Part of Project Personality by @EdTheAlchemist.
### Structural Metrics:
- Words per Tweet
- Characters per word
- Uppercase letters per Tweet
- Punctuation marks per Tweet
- Hashtags per Tweet
- Mentions per Tweet
- Links per Tweet
- Emojis per Tweet
- Emoticons per Tweet
- Consecutive repeated characters per word (ex. "hmmm")
- Consecutive repeated words per Tweet (ex. "pls pls pls")
### Regression Models:
- Mean regression (baseline)
- Linear regression
- Support vector regression
### Notes:
- Personality traits based on Five Factor Model
- Overwrite core.py from "emoji" library with the one in this repo
- Raw data available only to authors
### Directory tree/file descriptions:
```
root
├── figures
│ └── ...all plots per trait-metric pair output by regression.py
├── practice-data
│ └── ...practice data files
├── preprocessed-data
│ ├── tweet_tweet_pp.csv (not in repo due to file size)
| | - all preprocessed tweets
| | - output of preprocessing.py on project-data/twitter_tweet.csv
│ ├── tweet_tweet_pp_practice.csv
| | - output of preprocessing.py on practice-data/tweet_tweet.csv
│ ├── twt_user_masterlist.csv
| | - info of all twt users
│ ├── twt_valid_user_masterlist.csv
| | - info of all valid twt users
│ └── user_metrics.csv
| - user metrics for all *valid* twt users
├── project-data (not in repo due to file size)
│ ├── user.csv
│ ├── twitter_tweet.csv
│ ├── twitter_data.csv
│ └── personality_test.csv
├── .gitignore
├── changelog.txt
├── core.py
| - used instead of core.py in python emoji library
| - tokenizes emojis
├── count.py
| - py3, counts metrics for each user
| - input:
| - preprocessed-data/tweet_tweet_pp.csv
| - preprocessed-data/twt_user_masterlist.csv
| - preprocessed-data/twt_valid_user_masterlist.csv
| - output:
| - preprocessed-data/user_metrics.csv
├── emoticons.txt
| - additional emoticons recognized in twts
├── filter_users.py
| - py3, writes masterlist and filters invalid users
| - input:
| - project-data/twitter_data.csv
| - project-data/user.csv
| - project-data/personality_test.csv
| - output:
| - preprocessed-data/twt_user_masterlist.csv
| - preprocessed-data/twt_valid_user_masterlist.csv
├── preprocessing.py
| - py2, performs tokenization
| - input: project-data/twitter_tweet.csv
| - output: preprocessed-data/tweet_tweet_pp.csv
├── project-data.zip
├── README.md
├── regression.py
| - py3, performs regression and outputs plots
| - input:
| - preprocessed-data/user_metrics.csv
| - preprocessed-data/twt_valid_user_masterlist.csv
| - output:
| - results.txt
| - plots in figures/
└── results.txt
- RMSE & R^2 scores for regression on all trait-metric pairs
```