Twitter based sentimental analysis of Covid-19 observations

被引:3
|
作者
Vijayaraj, A. [1 ]
Bhavana, K. [1 ,2 ]
SreeDurga, S. [1 ,2 ]
Naik, S. Lokesh [2 ]
机构
[1] Vignans Fdn Sci Technol & Res, Dept Informat Technol, Guntur 522213, Andhra Pradesh, India
[2] MLR Inst Technol, Dept Comp Sci & Engn, Hyderabad, India
关键词
Social media; Sentimental analysis; Corona pandemic; Twitter; Polarity; Emotion;
D O I
10.1016/j.matpr.2022.05.194
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The emergence of social media has provided people with the opportunity to express their feelings and thoughts about everything and everything in their lives. There is a massive amount of textual stuff available, and approaches are required to make meaningful use of the information provided by isolating and evaluating the different types of text. Sentimental Analysis is a method of obtaining a human being's point of view through mining his or her emotions. The entire world is sharing their thoughts on social media on the Corona Pandemic that is now underway. This research presents an analysis of attitudes in order to determine whether or not people are optimistic in the face of a difficult circumstance. The technique of polarity is employed by the paper in order to determine if an opinion is positive, negative, or nonpartisan [1]. In order to determine the polarity, the following three major keywords are used: "COVID", "Corona virus," and "COVID-19." Copyright (C) 2022 Elsevier Ltd. All rights reserved.
引用
收藏
页码:713 / 719
页数:7
相关论文
共 50 条
  • [21] Classification of COVID19 Tweets based on Sentimental Analysis
    Banik, Sagar
    Ghosh, Aniket
    Banik, Sumit
    Mukherjee, Anupam
    2021 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI), 2021,
  • [22] Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models
    Chintalapudi, Nalini
    Battineni, Gopi
    Amenta, Francesco
    INFECTIOUS DISEASE REPORTS, 2021, 13 (02) : 329 - 339
  • [23] Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities
    Li, Xiaolin
    Li, Zhiyi
    Tian, Yahe
    ELECTRONICS, 2021, 10 (23)
  • [24] Negative COVID-19 Vaccine Information on Twitter: Content Analysis
    Yiannakoulias, Niko
    Darlington, J. Connor
    Slavik, Catherine E.
    Benjamin, Grant
    JMIR INFODEMIOLOGY, 2022, 2 (02):
  • [25] NUTRITION INFORMATION POST COVID-19: A TWITTER CONTENT ANALYSIS
    Tomar, Shagun
    Gupta, Manisha
    Rani, Madhu
    Shyam, Hari Shankar
    Ujjawal, Nishtha
    ASIA PACIFIC JOURNAL OF HEALTH MANAGEMENT, 2023, 18 (02):
  • [26] COVID-19 pandemic and the economy: sentiment analysis on Twitter data
    Fano, Shira
    Toschi, Gianluca
    INTERNATIONAL JOURNAL OF COMPUTATIONAL ECONOMICS AND ECONOMETRICS, 2022, 12 (04) : 429 - 444
  • [27] Coping with the COVID-19 crisis: an analysis of Twitter communication of companies
    Chong, Sabrina
    Momin, Mahmood
    PACIFIC ACCOUNTING REVIEW, 2021, 33 (05) : 603 - 615
  • [28] Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic
    Vaiyapuri, Thavavel
    Jagannathan, Sharath Kumar
    Ahmed, Mohammed Altaf
    Ramya, K. C.
    Joshi, Gyanendra Prasad
    Lee, Soojeong
    Lee, Gangseong
    SUSTAINABILITY, 2023, 15 (08)
  • [29] Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response
    Okango E.
    Mwambi H.
    Annals of Data Science, 2022, 9 (01) : 175 - 186
  • [30] Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline
    Karagkiozidou, Makrina
    Koukaras, Paraskevas
    Tjortjis, Christos
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART II, 2022, 647 : 350 - 359