Analyzing public sentiment around COVID from Tweets & Facebook posts


As COVID-19 led to unprecedented uncertainty & chaos in the daily lives of billions around the globe, our client wanted to help Public Policy Makers, Governments and MNCS listen to what the masses were saying on social media regarding the pandemic - and the overall sentiment around the interventions which local authorities were taking in their respective geographies. 

Their model was to synthesize thousands of tweets, posts and comments to assess overall public sentiment and uncover the broad themes & insights embedded within them. We were happy to help feed their model by labeling sample sets of such social media messages - all the while ensuring complete user anonymity and data privacy, ofcourse. 

Year: 2021

Tools used:

Challenges we faced: Often the same message, post or comment had multiple tonalities (e.g. negative/critical of government in the beginning, positive/hopeful by the conclusion). This made it quite difficult to assign static labels for overlapping classes in the same data. 

Posts also could contain multiple languages (often using colloquial or slang) - which meant we needed to onboard labellers conversational in those specific languages in-order to properly perform our task.

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