Psychotypes analysis by Myers–Briggs Type Indicator

HSE ML course project (2nd year)

Team:

• Kuznetsov Nikita
• Levin Mark

Installing dependencies

foo@bar:~$ pip3 install -r requirements.txt

Source data description

There are two datasets:

• dataset from kaggle.com, csv file containing two columns -- type (psychotype class, 16 in total) and posts (a person’s post on a social network in text format).
• For additional data, we manually parsed articles from the forum written by people with psychotypes designated mbti.

Our purposes

Write a model that determines 1 of 16 MBTI psychotypes of a person based on the written text. A telegram bot was also written to interact with users. Improve metric results by adding additional data from the forum mentioned above.

Used algorithms

• For preprocessing, we've removed punctuation and unnecessary information that didn't contain meangful text, conducted experiments with lemmatization and stemming, stemming turned out to be better.
• We have chosen TF-IDF to create embeddings.
• There was no explicit choice regarding the models; experiments were carried out with logistic regression, catboost, xgboost, and random forest.

Metrics

There is a classification problem with highly unbalanced classes (confirmation of this is below, a graph correlating the number of posts of people with psychotype classes), so we decided to take F1 score.

Results

The results from stemming were slightly better than those from lemmatization. Also, after adding an additional dataset, the results increased by 0.02 on average. (more details - in jupyter notebooks)

There are some of them (with supplemented dataset):

• Catboost (stemming): 0.676
• Xgboost (stemming): 0.671
• LogisticRegression (stemming): 0.645
• Catboost (lemmatization): 0.671
• Xgboost (lemmatization): 0.664
• LogisticRegression (lemmatization): 0.621
• RandomForestClassifier (stemming): 0.432