a diagnostic system for detecting covid-19 patients depending on lexicon semantic and biterm topic model-based feature selection on whatsapp messages classification

Covid-19 has created an urgent need for innovative detection methods. this study presents a novel approach to identifying potential covid-19 patients by analyzing their whatsapp messages using advanced natural language processing techniques. our methodology combines word2vec embeddings with lexical-semantic enrichment using conceptnet, creating a comprehensive system that can detect subtle linguistic patterns associated with covid-19 symptoms and experiences. the system processes whatsapp messages through multiple stages: initial data collection, word2vec embedding, lexicon semantic enhancement, vector-space model creation, biterm topic model-based feature selection, and finally, naive bayes classification. by enriching the language model with synonyms and capturing complex semantic relationships, our approach can identify potential covid-19 cases based on how people describe their symptoms and experiences in everyday conversations. we tested the system on a sample of diverse whatsapp messages, achieving promising results in distinguishing between messages from covid-19 patients and healthy individuals. the system successfully identified both explicit statements of covid-19 status and more subtle descriptions of symptoms, while correctly classifying non-covid related messages with high confidence. while this method shows potential as a non-invasive and scalable screening tool, it should be viewed as complementary to existing diagnostic approaches rather than a replacement. further large-scale testing is needed to fully validate the system's reliability and effectiveness in real-world applications.