NLP as a Tool for Mental Health Classification
The use of natural language processing (NLP) techniques in mental health classification has emerged as a significant area of research. Mental illness has become increasingly prevalent in our society, causing distress and impacting the overall well-being of individuals. Understanding the complex associations and risk factors associated with mental illness requires the analysis of various textual data sources, including social media posts, interviews, and clinical notes. NLP methods have shown promising advancements in empowering proactive mental healthcare and aiding in early diagnosis.
In a narrative review conducted over the past decade, a total of 399 studies from 10,467 records were analysed to explore the use of NLP in mental illness detection. The review revealed an upward trend in research focused on mental illness detection using NLP. Deep learning methods, which have gained significant attention, have demonstrated superior performance compared to traditional machine learning approaches.
The study also provides recommendations for future research directions in this domain. It suggests the development of novel detection methods, exploring deep learning paradigms, and creating interpretable models. Topic models have proven useful in understanding the differences in language usage between individuals with depression and those without. Supervised topic models have shown promising results in detecting depression by analyzing linguistic signals.
Data cleansing is an essential step in the preprocessing of textual data for mental health classification. Techniques such as tokenization, stop word removal, handling null entries, punctuation removal, and lemmatization are commonly employed. After conducting preliminary exploratory data analysis (EDA), it was decided to remove words with two or fewer characters.
To gain further insights into the data, EDA techniques such as bar plots, word clouds, and cosine similarity matrices were utilized. Feature engineering played a crucial role in the classification process, and techniques such as count vectorization, TF-IDF vectorization, GloVe embeddings, and Latent Dirichlet Allocation (LDA) were employed.
LDA, in particular, provides a measure of how well a given topic model has generalized to unseen documents using the perplexity score. A lower perplexity score indicates better performance in predicting unseen data.
In certain scenarios, character-level TF-IDF vectors have shown advantages over word-level representations. They offer robustness to spelling mistakes and out-of-vocabulary words by focusing on the character-level representation of the text. Additionally, character-level models can capture more morphological information, especially in languages with complex morphology, as they are less affected by inflections, derivations, and compound words. Moreover, character-level models can capture some aspects of syntax and semantics, further enhancing their effectiveness in mental health classification tasks.
Binary classifier models such as Nave Bayes, logistic regression, support vector machines, and SGD-Huber were applied to the data. The results obtained from these models can provide valuable insights into the classification of mental health-related texts.
In conclusion, NLP techniques have shown great potential for empowering mental healthcare and aiding in the early detection of mental illnesses. The use of deep learning methods, topic modelling, and feature engineering techniques has demonstrated promising results in this domain. Continued research in developing novel approaches, exploring deep learning paradigms, and interpreting models will further enhance the capabilities of NLP in mental health classification.