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

Abstract

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.

Chatbot Interaction with Artificial Intelligence: human data augmentation with T5 and language transformer ensemble for text classification

“…For these reasons, such approaches are rapidly forming State-of-the Art scores for many NLP problems (Tenney et al 2019). For text data in particular these include generation Radford et al 2019), question answering (Shao et al 2019;Lukovnikov et al 2019), sentiment analysis (Naseem et al 2020;Shangipour ataei et al 2020), translation (Zhang et al 2018;Wang et al 2019b;Di Gangi et al 2019), paraphrasing (Chada 2020;Lewis et al 2020), and classification (Sun et al 2019;Chang et al 2019). According to (Vaswani et al 2017), Transformers are based on calculation of scaled dotproduct attention units.…”

“…For these reasons, such approaches are rapidly forming State-of-the Art scores for many NLP problems [5]. For text data in particular these include generation [6,7], question answering [8,9], sentiment analysis [10,11], translation [12][13][14], paraphrasing [15,16], and classification [17,18].…”

Emotion Analysis of Tweets Banning Education in Afghanistan

“…Persian is one such language, where there is currently not much research and limited availability of these types of datasets. Despite the relatively limited previous work on emotion detection in Persian language, there is some work on resource creation in the related area of Sentiment Analysis, such as the SentiPers dataset (Hosseini et al, 2018), the Digikala dataset (Zobeidi et al, 2019) and the Pars-ABSA dataset (Ataei et al, 2019) , all based based on Iranian user comments.…”

Using of Transformers Models for Text Classification to Mobile Educational Applications

“…Por lo que no deja de ser un proceso complejo donde interviene el léxico, la semántica, la sintaxis y la pragmática [1] [2]. El campo de aplicación del PLN es muy diverso y existen varias líneas de investigación como el análisis de sentimientos [3], la clasificación de preguntas [6] o la clasificación de textos [4] [5], este último consiste en categorizar o etiquetar textos en clases organizadas según su contenido.…”