Traits Predictor

Brief Introduction

This is a simple Python personality predictor. Basically, it will cluster users using Big Five test style classification, and try to group users by the same trait to assign trait scores.

Features

• Predicting users' personality traits.
• Grouping people by users' personality trait

Methods

Data Pre-processing

• Unigram
• Word frequency

Classification

• Stochastic Gradient Descent
• Random Forest
• Multinomial Naive Bayes
• Bernoulli Naive Bayes
• Gradient Boost

Regression

• Ridge regression
• Lasso regression
• SGD regression
• Random Forest regression
• KNN regression
• Gradient Boosting regression
• Decision Tree regression

Model Selection

For each trait:

• Classification: model with the highest 10-fold cross validation f1 score will be selected.
• Regression: model with the lowest MSE and MAE will be selected.
• Grouping: taking median as score, if the absolute difference between mean and median is less than 0.1. Median will be assigned to classified test and validation data.

Data

Validation user selection is randomly selected from here, and the word feature data is collected from here. For the training data, is collected from here.

Files

• fileProcess.py: pulling data from twitter given some test usernames.
• tweetProcess.py: data processing of raw data pulled from fileProcess.py
• featureExtraction.py: feature extraction and I/O to better format.
• featureBuild.py: classifying users into clusters, using K-means, based on their tweets' feature. // no longer active.
• trainProcess.py: training data process.
  • Using Machine Learning techniques. This training process is aimed to predict traits' category, i.e., predict if user is an openness person.
  • Using regression method to get score predictions for each trait.
  • Grouping scores from same labeled trait in training dataset.
• trainBuild.py: training data pre-process and associated I/O.
• modelRun.py: model application on validation dataset.

Citation

• Nasukawa, T., & Yi, J. (2003, October). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77). ACM.
• Yang, H., & Li, Y. (2013). Identifying user needs from social media. IBM Research Division, San Jose, 11.
• Gou, L., Zhou, M. X., & Yang, H. (2014, April). KnowMe and ShareMe: understanding automatically discovered personality traits from social media and user sharing preferences. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems (pp. 955-964). ACM.
• Vinciarelli, A., & Mohammadi, G. (2014). A survey of personality computing. IEEE Transactions on Affective Computing, 5(3), 273-291.
• Mohammad, S., Zhu, X., Martin, J.: Semantic role labeling of emotions in tweets. In: Proceedings of the WASSA, pp. 32–41 (2014)
• Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., ... & De Cock, M. (2016). Computational personality recognition in social media. User modeling and user-adapted interaction, 26(2-3), 109-142.
• Celli, F., Pianesi, F., Stillwell, D., & Kosinski, M. (2013, June). Workshop on computational personality recognition (shared task). In Proceedings of the Workshop on Computational Personality Recognition.