BScFilter: A Deep Learning Approach for Sports Comments Filtering in a Resource Constraint Language

The escalating demand for live sports streaming has catalyzed a profound media transformation, favoring digital platforms over traditional mass media. However, this shift has also exposed the dark side of online interactions, including gambling and offensive comments, detracting from the experience of regular viewers. To address this, we present BScFilter, a pioneering Deep Learning approach for filtering sports comments in a resource-constrained environment. Our aim is to create a supportive viewer environment by automatically detecting and categorizing comments as gamble, hate, or sports-related. Leveraging our own developed dataset of 6012 annotated Bengali sports comments, we explore a range of ML and DL algorithms. The hybrid CNN+BiLSTM model with Keras embedding emerges as the top performer, achieving an impressive F1-score of 96.87%. We conduct comprehensive quantitative and qualitative analyses, revealing the strengths and limitations of our approach. While displaying promising outcomes, BScFilter offers an effective remedy to cultivate a respectful digital atmosphere for sports lovers, attenuating the influence of detrimental comments.