We use LSTM (Long Short-Term Memory) and BERT based models to carry out modeling and visualisation of the tweets based on Covid-19 in the year 2020. We further correlate various emotions with each tweet and see how the world responded to the pandemic in the internet.
License: MIT License
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covid19_sentinentanalysis's Introduction
COVID19_sentinentanalysissocialmedia
The global pandemic that has raged the world for the past year is here to stay. In this repo, we try to predict the various sentiments associated with a particular tweet after training on readily available datasets, the links to which have been attached below.
The multi-label benchmark dataset can be retrieved from the first reference down below.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic, Yang, Qiang and Alamro, Hind and Albaradei, Somayah and Salhi, Adil and Lv, Xiaoting and Ma, Changsheng and Alshehri, Manal and Jaber, Inji and Tifratene, Faroug and Wang, Wei and others https://arxiv.org/pdf/2006.10842.pdf (Note: If you want to use the labled tweets, please mail to qiang.yang[AT]kaust[dot]edu[dot]sa to get the pwd for the zip filefolder.)
Semeval-2018 Task 1: Affect in Tweets. Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. In Proceedings of the International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, USA, June 2018.
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