Evaluating Student Depression Detection from Social Media Post Using Multinomial Naïve Bayes Text Vectorization and Randomizedsearchcv
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Purpose - The complexities of recognizing student depression make it a promising area for exploring the effectiveness of various text vectorization techniques such as `CountVectorizer`,
`TfidfVectorizer`, and `HashingVectorizer` in machine learning applications. These techniques can help identify patterns and symptoms by analyzing large datasets like social media activity which may reveal indicators of depression that traditional diagnostic methods might miss.
Method - In recognizing student depression through machine learning, the methodology follows the Knowledge Discovery in Databases (KDD) process which guides each stage of data handling and analysis.
Results - Based on the result of the evaluation of different text vectorization techniques, the `CountVectorizer` achieved the highest classification accuracy at 87%, ranking it first. `TfidfVectorizer` follows closely with an accuracy of 86%, ranking second. Lastly, `HashingVectorizer` achieved a lower accuracy of 77%, placing it third in performance.
Conclusion – The study reaffirms that traditional vectorization methods like `CountVectorizer` and `TfidfVectorizer` remain highly effective for text classification tasks and that method selection should be guided by the specific needs of the application and characteristics of the data.
Recommendation - Future work will center on combining these techniques or probing more advanced embedding methods which better capture deeper semantic relations. The generalization of such vectorizers on other datasets and domains will be further illustrated in detail by their robustness and performance.
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