With the increasing popularity of browser extensions, there is a growing concern about malicious actors exploiting this platform to distribute malware. Existing solutions rely on manual review processes or static analysis methods, which are insufficient in detecting complex and evolving threats. In this study, we investigate the potential of Natural Language Processing (NLP) for automatically classifying users’ comments in the Chrome Web Store to identify malicious extensions. We propose a novel framework called CoTH that leverages NLP techniques to analyse the textual feedback provided by users and detect patterns indicative of malicious activity. We evaluate the accuracy of our model using a dataset of user reviews and achieve a recall of 0.76 demonstrating its effectiveness in identifying malicious extensions. Our findings suggest that NLP-based comment analysis can be a valuable addition to existing security measures, providing an opportunity for more accurate and efficient detection of malware in the Chrome Web Store.