Nusratul Jannat


2022

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CUET-NLP@DravidianLangTech-ACL2022: Investigating Deep Learning Techniques to Detect Multimodal Troll Memes
Md Hasan | Nusratul Jannat | Eftekhar Hossain | Omar Sharif | Mohammed Moshiul Hoque
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

With the substantial rise of internet usage, social media has become a powerful communication medium to convey information, opinions, and feelings on various issues. Recently, memes have become a popular way of sharing information on social media. Usually, memes are visuals with text incorporated into them and quickly disseminate hatred and offensive content. Detecting or classifying memes is challenging due to their region-specific interpretation and multimodal nature. This work presents a meme classification technique in Tamil developed by the CUET NLP team under the shared task (DravidianLangTech-ACL2022). Several computational models have been investigated to perform the classification task. This work also explored visual and textual features using VGG16, ResNet50, VGG19, CNN and CNN+LSTM models. Multimodal features are extracted by combining image (VGG16) and text (CNN, LSTM+CNN) characteristics. Results demonstrate that the textual strategy with CNN+LSTM achieved the highest weighted f1-score (0.52) and recall (0.57). Moreover, the CNN-Text+VGG16 outperformed the other models concerning the multimodal memes detection by achieving the highest f1-score of 0.49, but the LSTM+CNN model allowed the team to achieve 4th place in the shared task.

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CUET-NLP@DravidianLangTech-ACL2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews
Nasehatul Mustakim | Nusratul Jannat | Md Hasan | Eftekhar Hossain | Omar Sharif | Mohammed Moshiul Hoque
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people’s opinions on diverse issues. Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advancement of the multimodality of the content users on social networking sites. This paper presents a technique for classifying multimodal sentiment using the text modality into five categories: highly positive, positive, neutral, negative, and highly negative categories. A shared task was organized to develop models that can identify the sentiments expressed by the videos of movie reviewers in both Malayalam and Tamil languages. This work applied several machine learning techniques (LR, DT, MNB, SVM) and deep learning (BiLSTM, CNN+BiLSTM) to accomplish the task. Results demonstrate that the proposed model with the decision tree (DT) outperformed the other methods and won the competition by acquiring the highest macro f1-score of 0.24.