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Found 33 papers, 10 papers with code
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities.
STHAL: Location-mention Identification in Tweets of Indian-context
We investigate the problem of extracting Indian-locations from a given crowd-sourced textual dataset.
Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection
As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it.
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
In this work, we propose a novel task, MEMEX - given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme.
Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation
Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances.
Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling
Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space.
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes.
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis
Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications.
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification
The surface-level signals expressed by a social-text itself may not be adequate for such tasks; therefore, recent methods attempted to incorporate other intrinsic signals such as user behavior and the underlying graph structure.
Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering
A structured counseling conversation may contain discussions about symptoms, history of mental health issues, or the discovery of the patient's behavior.
Auxiliary Task Guided Interactive Attention Model for Question Difficulty Prediction
Hence we soft label another dataset with a model fine-tuned to predict Bloom's labels to demonstrate the applicability of our method to datasets with only difficulty labels.
DISARM: Detecting the Victims Targeted by Harmful Memes
Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
Detecting Harmful Memes and Their Targets
In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
Related Tasks can Share! A Multi-task Framework for Affective language
Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity.
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis
In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis.
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis
In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both.
Language-Agnostic Model for Aspect-Based Sentiment Analysis
In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Sentiment analysis has immense implications in e-commerce through user feedback mining.
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis
We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz.
Ranked #6 on
Multimodal Sentiment Analysis
on MOSI
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction
In this paper, through multi-task ensemble framework we address three problems of emotion and sentiment analysis i. e. "emotion classification & intensity", "valence, arousal & dominance for emotion" and "valence & arousal} for sentiment".
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality
Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis
In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis.
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features
This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017).
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text
The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features.
IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation
This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}.
IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis
In this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) along with feature based models to identify optimistic or pessimistic sentiments associated with companies and stocks in financial texts.
A Hybrid Deep Learning Architecture for Sentiment Analysis
The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification.
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation
Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
