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Found 23 papers, 5 papers with code
Towards Building Affect sensitive Word Distributions
Our work addresses the question: can affect lexica improve the word representations learnt from a corpus?
Aff2Vec: Affect--Enriched Distributional Word Representations
Human communication includes information, opinions, and reactions.
Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email
We present a novel approach to model human frustration in text.
Diachronic degradation of language models: Insights from social media
It asks the question: given that the social media platform and its users remain the same, how is language changing over time?
Sentiment and Sarcasm Classification with Multitask Learning
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
Variational Fusion for Multimodal Sentiment Analysis
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Ranked #1 on
Emotion Recognition in Conversation
on SEMAINE
Affect Enriched Word Embeddings for News Information Retrieval
Distributed representations of words have shown to be useful to improve the effectiveness of IR systems in many sub-tasks like query expansion, retrieval and ranking.
Multi-label Categorization of Accounts of Sexism using a Neural Framework
Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways.
Generating Formality-Tuned Summaries Using Input-Dependent Rewards
Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content.
"To Target or Not to Target": Identification and Analysis of Abusive Text Using Ensemble of Classifiers
With rising concern around abusive and hateful behavior on social media platforms, we present an ensemble learning method to identify and analyze the linguistic properties of such content.
CaM-Gen:Causally-aware Metric-guided Text Generation
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation
A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed as the brand personality of the company.
Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification
Sexism, a form of oppression based on one{'}s sex, manifests itself in numerous ways and causes enormous suffering.
Recognizing Emotion Cause in Conversations
We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines.
Ranked #1 on
Recognizing Emotion Cause in Conversations
on RECCON
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction
Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users' language preferences.
WikiTalkEdit: A Dataset for modeling Editors' behaviors on Wikipedia
We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor.
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer
Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer.
"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning
We exploit context by pretraining our model with datasets of three tasks: news image captioning where the news article is the context, contextual visual entailment, and keyword extraction from the context.
CaM-Gen: Causally Aware Metric-Guided Text Generation
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
AUTOSUMM: Automatic Model Creation for Text Summarization
Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets.
Leveraging Mental Health Forums for User-level Depression Detection on Social Media
We leverage accounts of depression taken from this dataset to infuse domain-specific elements into our framework.
