{"title":"Contextual emotion detection using ensemble deep learning","authors":"Asalah Thiab , Luay Alawneh , Mohammad AL-Smadi","doi":"10.1016/j.csl.2023.101604","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>Emotion detection from online textual information is gaining more attention due to its usefulness in understanding users’ behaviors and their desires. This is driven by the large amounts of texts from different sources such as social media and shopping websites. Recent studies investigated the benefits of deep learning in the detection of emotions from textual conversations. In this paper, we study the performance of several deep learning and transformer-based models in the classification of emotions in English conversations. Further, we apply </span>ensemble learning using a majority voting technique to improve the overall classification performance. We evaluated our proposed models on the SemEval 2019 Task 3 public dataset that categorizes emotions as </span><em>Happy</em>, <em>Angry</em>, <em>Sad</em>, and <em>Others</em>. The results show that our models can successfully distinguish the three main classes of emotions and separate them from <em>Others</em> in a highly imbalanced dataset. The transformer-based models achieved a micro-averaged F1-score of up to 75.55%, whereas the RNN-based models only reached 67.03%. Further, we show that the ensemble model significantly improves the overall performance and achieves a micro-averaged F1-score of 77.07%.</p></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230823001237","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Emotion detection from online textual information is gaining more attention due to its usefulness in understanding users’ behaviors and their desires. This is driven by the large amounts of texts from different sources such as social media and shopping websites. Recent studies investigated the benefits of deep learning in the detection of emotions from textual conversations. In this paper, we study the performance of several deep learning and transformer-based models in the classification of emotions in English conversations. Further, we apply ensemble learning using a majority voting technique to improve the overall classification performance. We evaluated our proposed models on the SemEval 2019 Task 3 public dataset that categorizes emotions as Happy, Angry, Sad, and Others. The results show that our models can successfully distinguish the three main classes of emotions and separate them from Others in a highly imbalanced dataset. The transformer-based models achieved a micro-averaged F1-score of up to 75.55%, whereas the RNN-based models only reached 67.03%. Further, we show that the ensemble model significantly improves the overall performance and achieves a micro-averaged F1-score of 77.07%.
期刊介绍:
Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language.
The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.