Stance detection in online discussions

01/02/2017
by   Peter Krejzl, et al.
0

This paper describes our system created to detect stance in online discussions. The goal is to identify whether the author of a comment is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific features. The system was originally developed to detect stance in English tweets. We adapted it to process Czech news commentaries.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/13/2018

Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models

This paper reports an increment to the state-of-the-art in hate speech d...
research
08/26/2019

Detecting Toxicity in News Articles: Application to Bulgarian

Online media aim for reaching ever bigger audience and for attracting ev...
research
07/28/2020

Deep Learning Brasil – NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

In this paper, we describe a methodology to predict sentiment in code-mi...
research
09/03/2017

A Semi-Supervised Approach to Detecting Stance in Tweets

Stance classification aims to identify, for a particular issue under dis...
research
06/17/2016

A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection

We investigate the novel task of online dispute detection and propose a ...
research
03/23/2018

Stance Detection on Tweets: An SVM-based Approach

Stance detection is a subproblem of sentiment analysis where the stance ...
research
03/14/2022

CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection

We present CoNTACT: a Dutch language model adapted to the domain of COVI...

Please sign up or login with your details

Forgot password? Click here to reset