In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts. This dataset comprises public comments on news and videos collected from social media covering 13 different domains, including politics, educat
ion, and agriculture. These comments are labeled with one of the polarity labels, namely positive, negative, and neutral. One significant characteristic of the dataset is that each of the comments is noisy in terms of the mix of dialects and grammatical incorrectness. Our experiments to develop a benchmark classification system show that hand-crafted lexical features provide superior performance than neural network and pretrained language models. We have made the dataset and accompanying models presented in this paper publicly available at https://git.io/JuuNB.
Bengali is a low-resource language that lacks tools and resources for profane and obscene textual content detection. Until now, no lexicon exists for detecting obscenity in Bengali social media text. This study introduces a Bengali obscene lexicon co
nsisting of over 200 Bengali terms, which can be considered filthy, slang, profane or obscene. A semi-automatic methodology is presented for developing the profane lexicon that leverages an obscene corpus, word embedding, and part-of-speech (POS) taggers. The developed lexicon achieves coverage of around 0.85 for obscene and profane content detection in an evaluation dataset. The experimental results imply that the developed lexicon is effective at identifying obscenity in Bengali social media content.
