Mirror Of Me

Problem Scenario

Personality of a person encircles every aspect of life. It describes the pattern of thinking, feeling and characteristics that predict and describe an individual’s behaviour and also influences daily life activities including emotions, preference, motives and health . The increasing use of Social Networking Sites, such as Twitter and Facebook have propelled the online community to share ideas, sentiments, opinions, and emotions with each other; reflecting their attitude, behaviour and personality. Obviously, a solid connection exists between individual’s temperament and the behaviour they show on social networks in the form of comments or tweets . Nowadays personality recognition from social networking sites has attracted the attention of researchers for developing automatic personality recognition systems.

Our Approach

However, the major issue associated with the aforementioned studies is the skewness of the datasets, i.e. presence of imbalanced classes with respect to different personality traits. This issue mainly contributes to the performance degradation of personality recognition system. To address the aforementioned issue different techniques are available for minimizing the skewness of the dataset, like Over-sampling, Under-sampling and hybrid-sampling [9]. Such techniques, when applied on the imbalanced datasets in different domain, have shown promising performance in terms of improved accuracy, recall, precision, and F1-score

Potential Client

Tools Used

We also utilize the Natural Language Toolkit (NLTK) library for much of our text preprocessing, Numpy for matrix computations, sk-learn for conventional learning algorithms, and PyTorch for deep learning.

Data Description

We obtained our data from the (MBTI) Myers-Briggs Personality Type Dataset from Kaggle. It provides the text of the 45-50 most recent social media posts for 8,600 users along with the user’s MBTI personality type.

This gives us 422,845 total labeled points in the form (post text, MBTI type). The posts are drawn from the PersonalityCafe online forum, a platform for all kinds of >conversations and discussions, and they obtain the labels by allowing the user to input MBTI type as account info. Data in this dataset was collected from an Internet forum and >after analysing the content of the dataset.

Non-uniform representation of MBTI types in the dataset that is not commensurate with the actual proportions of MBTI types in the general population was the most important reason for this.

It was determined that this is because the data was collected from an Internet forum created for discussion about personality type and MBTI types were repeated too many times in >the posts. This may also affect the accuracy of the model. As a result, NLTK was used to remove the MBTI types from the dataset.

Citation

• https://ieeexplore.ieee.org/document/9578983
• https://www.researchgate.net/publication/314100144_Social_media_user_personality_classification_using_computational_linguistic
• https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00459-1