Abstract:
Social media platforms have evolved into a ubiquitous hub for individuals to convey
their thoughts, emotions, and behaviours, allowing to generate insights into their personalities.
However, the available social media datasets are not diverse enough as they
often tend to over-represent certain groups of individuals such as young people. Further
class distribution is disproportionate, potentially restricting the generalizability and
accuracy of personality analysis. To address these challenges, this study suggests a
method using text augmentation techniques and machine learning to expand the dataset
and improve the effectiveness of the analysis using the Myers-Briggs Type Indicator
(MBTI) dataset of 430000 posts belonging to 8600 individuals from personalitycafe
forum. The dataset was split into 80% training and 20% testing sets and the training
dataset was later augmented and fed into the models. All the models were evaluated
using standard metrics such as accuracy, precision, and recall. Among the evaluated
models, Linear Regression classifier outperformed the other three machine learning
algorithms with an accuracy of 68.41%. The results are more uniformly distributed
across the classes when compared with the other three machine learning algorithms
Random Forest, Gradient Descent and XGBoost. Additionally, results showed that the
text augmentation strategies employing BERT contextual word embeddings improved
the model accuracy by 0.2%. A meagre improvement was observed due to the low quality
of the dataset, and lack of contextual understanding in augmented data. Computational
cost hindered the possibility of further improvement. Synonym-based augmentation
showed poor performance due to a lack of contextual understanding, whereas BERTbased
augmentation produced semantically and contextually relevant data, resulting
in improved performance. For future work, self-training reinforced models and transfer
learning need to be investigated to increase the model performance.
