Search Results for author:
Found 16 papers, 9 papers with code
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.
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.
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.
MUTE: A Multimodal Dataset for Detecting Hateful Memes
The exponential surge of social media has enabled information propagation at an unprecedented rate.
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.
