Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets

Title

Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets

Creator

Jyothsna R.; Rohini V.; Paulose J.

Description

Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Smart Innovation, Systems and Technologies, Vol-317, pp. 317-324.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

BERT; Deep learning; LSTM; Machine learning; Sentiment analysis; SVM; Twitter

Coverage

Jyothsna R., Christ University, Bangalore, India; Rohini V., Christ University, Bangalore, India; Paulose J., Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 21903018; ISBN: 978-981196067-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-6068-0_31

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145010365&doi=10.1007%2f978-981-19-6068-0_31&partnerID=40&md5=d53e6ff3872ff78ede9deb4a491cff80