Search Results for author:
Found 26 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.
Towards Bengali Word Embedding: Corpus Creation, Intrinsic and Extrinsic Evaluations
The semantic word analogy tasks achieved 44. 00% of accuracy while syntactic word analogy tasks obtained 36. 00%.
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.
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.
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@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.
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.
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).
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.
AraCovTexFinder: Leveraging the transformer-based language model for Arabic COVID-19 text identification
This gap hampers crucial COVID information retrieval and processing necessary for policymakers and health authorities.
CovTiNet: Covid text identification network using attention-based positional embedding feature fusion
Covid text identification (CTI) is a crucial research concern in natural language processing (NLP).
MUTE: A Multimodal Dataset for Detecting Hateful Memes
The exponential surge of social media has enabled information propagation at an unprecedented rate.
Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks
In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD).
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.
An Efficient K-means Clustering Algorithm for Analysing COVID-19
Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets.
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.
Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques
Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol.
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies
Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps.
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.
A Rule Based Expert System to Assess Coronary Artery Disease under Uncertainty
The coronary artery disease (CAD) involves narrowing and damaging the major blood vessels has become the most life threating disease in the world especially in south Asian reason.
Quantitative Methods Applications
Crime Prediction Using Spatio-Temporal Data
Research can help society to prevent and solve crime activates.
