Data Collection

Data is a critical component in training and evaluating machine learning models. For this project, a dataset comprising posts from Reddit's SuicideWatch and depression subreddits was utilized. The data was collected using the Pushshift API, which provided posts from SuicideWatch (from Dec 16, 2008, to Jan 2, 2021) and depression (from Jan 1, 2009, to Jan 2, 2021). Non-suicide posts were collected from the r/teenagers subreddit. This dataset consists of labeled posts, with each post marked as either suicide or non-suicide.

Data Preprocessing

Preprocessing the raw text data is essential for improving the quality of the input for machine learning models. The preprocessing steps include:

• Text Cleaning: Removing non-alphanumeric characters, numbers, and other irrelevant symbols.

• Tokenization: Breaking down text into individual tokens (words or phrases) for easier analysis.

• Stopword Removal: Eliminating common words like “the,” “is,” etc., which do not contribute significant meaning to the analysis.

• Lemmatization: Reducing words to their base form (e.g., “running” to “run”) to standardize the text.

Feature Extraction

To convert text into numerical format, feature extraction techniques are used. This enables machine learning models to process and learn from the data. Two methods of feature extraction are employed:

• TF-IDF (Term Frequency-Inverse Document Frequency): TF-IDF is used to calculate the importance of words in the text based on their frequency within a document and across the entire dataset.

• Word Embeddings: Pre-trained word embeddings, such as Word2Vec or GloVe, can be used to convert text into vector representations that capture semantic relationships between words.

These techniques ensure that the models can understand the meaning behind the words and their context within the posts.

Model Development

The model development process involves both machine learning and deep learning approaches to ensure that the system can effectively detect patterns in text related to suicidal ideation.

Machine Learning

We train several classical machine learning models to classify the text data. These include:

• K-Nearest Neighbors (KNN): A simple yet effective classification algorithm based on the proximity of data points.

• Logistic Regression: A statistical model used for binary classification tasks, particularly when the relationship between input features and target labels is linear.

• Multinomial Naive Bayes (NB): A probabilistic model ideal for text classification tasks with discrete features.

• Random Forest: An ensemble learning method that constructs multiple decision trees and aggregates their predictions to improve accuracy.

Deep Learning

To capture the sequential nature of text data and contextual dependencies, a custom deep learning model is developed. The architecture consists of:

• BiLSTM (Bidirectional Long Short-Term Memory): A type of Recurrent Neural Network (RNN) that can capture long-range dependencies in the text while processing it in both forward and backward directions.

• RNN (Recurrent Neural Network): A model designed for sequence data that remembers information from previous inputs, making it suitable for text analysis.

The combination of these models allows for a more nuanced understanding of the data, especially when dealing with complex language patterns indicative of suicidal ideation.