Khondoker Ittehadul Islam


2022

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EmoNoBa: A Dataset for Analyzing Fine-Grained Emotions on Noisy Bangla Texts
Khondoker Ittehadul Islam | Tanvir Yuvraz | Md Saiful Islam | Enamul Hassan
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

For low-resourced Bangla language, works on detecting emotions on textual data suffer from size and cross-domain adaptability. In our paper, we propose a manually annotated dataset of 22,698 Bangla public comments from social media sites covering 12 different domains such as Personal, Politics, and Health, labeled for 6 fine-grained emotion categories of the Junto Emotion Wheel. We invest efforts in the data preparation to 1) preserve the linguistic richness and 2) challenge any classification model. Our experiments to develop a benchmark classification system show that random baselines perform better than neural networks and pre-trained language models as hand-crafted features provide superior performance.

2021

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SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts
Khondoker Ittehadul Islam | Sudipta Kar | Md Saiful Islam | Mohammad Ruhul Amin
Findings of the Association for Computational Linguistics: EMNLP 2021

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, education, 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.