Can Twitter Help to Predict Outcome of 2019 Indian General Election: A Deep Learning Based Study

Agarwal, Amit; Toshniwal, Durga; Bedi, Jatin

doi:10.1007/978-3-030-43887-6_4

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…Some of the relevant studies are as follows. Agarwal et al [19] used long short-term memory (LSTM) networks and prominent word embedding for 41 million tweets for the 2019 Indian general elections where the predictions showed a close correlation with the actual results. Suciati et al [20] used machine learning to detect buzzer accounts that disseminate information deliberately for the 2019 Indonesian Presidential elections.…”

Section: Introductionmentioning

confidence: 99%

Biden vs Trump: Modeling US General Elections Using BERT Language Model

Chandra¹,

Saini

2021

IEEE Access

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Social media has played a crucial role in shaping the worldview during election campaigns. It has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots. Previous research indicates that understanding user behaviour, particularly in terms of sentiments expressed during elections, can indicate the election outcome. Recently, there has been tremendous progress in language modelling with deep learning via long short-term memory (LSTM) models and variants known as Bidirectional Encoder Representations from Transformers (BERT). Motivated by these innovations, we develop a framework to model the US general elections. We investigate if sentiment analysis can provide a means to predict election outcomes. We use the LSTM and BERT language models for Twitter sentiment analysis leading to the US 2020 presidential elections. Our results show that sentiment analysis can form a general basis for modelling election outcomes where the BERT model indicates Biden winning the elections.

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Section: Introductionmentioning

confidence: 99%