Flirting Words Detection Using Machine Learning Techniques

Abstract

Flirting is a form of interpersonal communication often involving subtle language cues, making it challenging to identify accurately. Detecting flirting words or patterns can be valuable in various applications, such as social media moderation, dating platforms, and conversational AI systems. This study explores machine learning techniques to detect flirting words or phrases in textual communication. The research creates a labeled dataset on flirtatious and non-flirtatious text samples, where NLP will be applied to preprocess and then feature-engineer the data. Multiple machine learning models, including supervised and unsupervised algorithms, are compared using their accuracy in flirting cue identification. The study also compares the model's accuracy enhanced by semantic understanding, the analysis of context, and sentiment detection. The results show that state-of-the-art models such as deep learning and transformer-based architectures like BERT have outperformed classical machine learning techniques to capture subtle aspects of flirting language. Thus, results highlight the role of contextual and cultural factors for conversational AI in identifying flirtation and underscore the future directions of enhancing machine learning that would be able to comprehend and emulate human conversation better.