Sarcasm Detection in Tweets with BERT and GloVe Embeddings

06/20/2020

∙

Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response.

READ FULL TEXT

Sarcasm Detection in Tweets with BERT and GloVe Embeddings

Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings

New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings

Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT

Représentations lexicales pour la détection non supervisée d'événements dans un flux de tweets : étude sur des corpus français et anglais

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

Homonym Identification using BERT – Using a Clustering Approach

Identifying causal associations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021

Sarcasm Detection in Tweets with BERT and GloVe Embeddings

Related Research

Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings

New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings

Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT

Représentations lexicales pour la détection non supervisée d'événements dans un flux de tweets : étude sur des corpus français et anglais

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

Homonym Identification using BERT – Using a Clustering Approach

Identifying causal associations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021