Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection

Sarcasm is a type of figurative language broadly adopted in social media and daily conversations. The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic. The best performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


 Ranked #1 on Sarcasm Detection on FigLang 2020 Reddit Dataset (using extra training data)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Sarcasm Detection FigLang 2020 Reddit Dataset BERT+Aspect-based approaches F1 0.737 # 1
Sarcasm Detection FigLang 2020 Twitter Dataset BERT F1 0.731 # 2

Methods


No methods listed for this paper. Add relevant methods here