3 citations · 9 across the 6 of their papers we have counts for
7 papers · 1 filter
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis
Pranav Narayanan Venkit, Mukund Srinath, Sanjana Gautam +4
We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets. Our i…
Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar +2
We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lea…
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias
Vipul Gupta, Pranav Narayanan Venkit, Hugo Laurençon +2
As language models (LMs) become increasingly powerful and widely used, it is important to quantify them for sociodemographic bias with potential for harm. Prior measures of bias ar…
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models
Pranav Narayanan Venkit, Mukund Srinath, Shomir Wilson
We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framewor…
Sociodemographic Bias in Language Models: A Survey and Forward Path
Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson +1
Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of res…
A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source
Pranav Venkit, Zeba Karishma, Chi-Yang Hsu +4
We widely use emojis in social networking to heighten, mitigate or negate the sentiment of the text. Emoji suggestions already exist in many cross-platform applications but an emoj…