Search Results for author:
Found 23 papers, 11 papers with code
M-BAD: A Multilabel Dataset for Detecting Aggressive Texts and Their Targets
Recently, detection and categorization of undesired (e. g., aggressive, abusive, offensive, hate) content from online platforms has grabbed the attention of researchers because of its detrimental impact on society.
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes
However, due to the proliferation of social media usage in recent years, sentiment analysis of memes is also a crucial research issue in low resource languages.
CUET-NLP@DravidianLangTech-ACL2022: Investigating Deep Learning Techniques to Detect Multimodal Troll Memes
Moreover, the CNN-Text+VGG16 outperformed the other models concerning the multimodal memes detection by achieving the highest f_1-score of 0. 49, but the LSTM+CNN model allowed the team to achieve 4^{th} place in the shared task.
CUET-NLP@DravidianLangTech-ACL2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews
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.
CUET-NLP@TamilNLP-ACL2022: Multi-Class Textual Emotion Detection from Social Media using Transformer
Recently, emotion analysis has gained increased attention by NLP researchers due to its various applications in opinion mining, e-commerce, comprehensive search, healthcare, personalized recommendations and online education.
COMBATANT@TamilNLP-ACL2022: Fine-grained Categorization of Abusive Comments using Logistic Regression
Both Logistic Regression (LR) and CNN+BiLSTM with FastText achieved the weighted F_1-score of 0. 39.
Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse.
Deciphering Hate: Identifying Hateful Memes and Their Targets
The dataset consists of 7, 148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target (i. e., Individual, Organization, Community, and Society).
Mad Libs Are All You Need: Augmenting Cross-Domain Document-Level Event Argument Data
Our approach leverages the intuition that Mad Libs, which are categorically masked documents used as a part of a popular game, can be generated and solved by LLMs to produce data for DocEAE.
Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection
Evaluation results demonstrate our proposed approach's effectiveness with F1-scores of $69. 7$% and $70. 3$% for the MUTE and MultiOFF datasets.
Chain-of-Thought Embeddings for Stance Detection on Social Media
Chain-of-Thought (COT) prompting has recently been shown to improve performance on stance detection tasks -- alleviating some of these issues.
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity
Additionally, we show generative LLMs significantly outperform existing encoder-based STS models when characterizing the semantic similarity between two texts with complex semantic relationships dependent on world knowledge.
Characterizing Information Seeking Events in Health-Related Social Discourse
To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse.
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task.
MUTE: A Multimodal Dataset for Detecting Hateful Memes
The exponential surge of social media has enabled information propagation at an unprecedented rate.
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
A Bengali emotion corpus consists of 6243 texts is developed for the classification task.
NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers
In the task, datasets provided in three languages including Tamil, Malayalam and Kannada code-mixed with English where participants are asked to implement separate models for each language.
NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes
We investigated the visual and textual features using CNN, VGG16, Inception, Multilingual-BERT, XLM-Roberta, XLNet models.
NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner
We propose three distinct models to identify hope speech in English, Tamil and Malayalam language to serve this purpose.
Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media
This paper illustrates a detail description of the system and its results that developed as a part of the participation at CONSTRAINT shared task in AAAI-2021.
TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network
This paper illustrates the details description of technical text classification system and its results that developed as a part of participation in the shared task TechDofication 2020.
SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews
In addition, a comparative analysis of the proposed technique with other machine learning algorithms presented.
Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naive Bayes
Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer's opinions or reviews in the online platform.
