Deep sentiment analysis with data augmentation in distance education during the pandemic

Sera Deniz Sosun, Bulent Tayfun, Yasemin Nukan, Irem Altun, Elif Berra Erik, Elif Yildirim, Busra Kocacinar, Fatma Patlar Akbulut

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

During the global Covid-19 pandemic, the shutdown of educational institutes has resulted in a phenomenal surge in online learning. Academic activities were shifted to online learning platforms to restrict the influence of COVID-19 and block its spread. For both students and parents, the efficiency of online learning is a major concern, particularly in terms of its suitability for students and teachers, as well as its technological applicability in various social situations. Before the online learning approach can be employed on such a big scale, such challenges must be viewed from different aspects. This study aims to assess the efficiency of online learning by examining individuals' sentiments toward it. Due to social media becoming such an essential form of communication, people's opinions can be observed on platforms like Twitter. The main motivation is to use a Twitter dataset featuring online learning-related tweets. Briefly, we focused on specifying the impact of the Covid-19 pandemic on education in many aspects and parameters by using tweets. We utilized natural language processing models for text classification with a gathered dataset that includes fetching tweets consisting of Covid-19 and education topics. We developed a fine-tuned Long short-term memory (LSTM) model that utilizes data augmentation for classifying the emotional states of individuals. With the deep sentiment analysis model that we proposed, we observed that the negative sentiments were experienced more.

Original languageEnglish
Title of host publicationProceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665488945
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 - Antalya, Turkey
Duration: 7 Sept 20229 Sept 2022

Publication series

NameProceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

Conference

Conference2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022
Country/TerritoryTurkey
CityAntalya
Period7/09/229/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • LSTM
  • RNN
  • deep learning
  • e-learning
  • natural language processing
  • sentiment analysis

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