Abstract
Abstract: Protests are an integral part of democracy and are a vital tool for the general public to convey their demands and/or discontentment to the ruling government. As voters return to term with any new rules, there are an increasing range of protests everywhere in the world for numerous socio-political reasons. With the advancement of technology, there has additionally been an exponential rise within the use of social media for the exchange of data and ideas. During this research, knowledge was gathered from the web site “twitter.com”, regarding farmers’ protest to know the feelings that the public shared on a global level. Sadly now since the Farm Laws are repealed, we have a tendency to aim to use this knowledge to know the general public stance on these laws, and whether or not it affected the government’s decision. This paper proposes a stance prediction deep learning model achieved after fine tuning the well known ULMFiT (Universal Language Model Fine-tuning) model by Howard and Ruder. Categories to be classified into are For (F), Against (A) and Neutral (N). Proposed model achieved an F1 score of 0.67 on our training and test data, which is essentially a labeled subset of the actual data. Keywords: Dataset, ULMFiT, deep learning, text classification, Language Model (LM)
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