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Topic modeling of video call transcripts (Zoom) using Latent Dirichlet Allocation (LDA).

If there is a phrase that has stuck to us coming out of 2020, it will be “stay at home, stay safe.” The coronavirus pandemic forced the world’s population to stay at home to help curb the spread of the virus and directly increased remote environments. This new social behavior has led to a surge in popularity and usage of numerous Video-conferencing services such as Zoom, Cisco WebEx, Microsoft Teams, Google Hangouts Meet, etc. The profound resonance with this new social distancing culture has led many to find many creative ways to virtually stay social through these video-conferencing services such as online meetings, schooling, concerts, ceremonies, fitness programs, etc.

These Video-conferencing services are suddenly being used for literally everything. Due to the increasing number of virtual meetings in organizations, joining numerous meetings at a given time might be unproductive and time-consuming for top-executives and employees. This project will aid in understanding these meetings without being present by identifying topics present and latent patterns via topic modeling.

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