Venkata Duvvuri, Chetan Kulkarni and Sritha Gogineni, Northeastern University, USA

COVID-19 pandemic has created a major impact around the world. Governments and businesses small or big around the world are facing unprecedent decisions to either close up or reopen or drive other policies based on the sentiment of people. While, understanding this sentiment and accompanying emotions has been researched especially in social media channels like Twitter, we propose a novel way to capture sentiment and emotions using intelligent chatbots (EmoBot) that reduces the participants biases inherent in prior analysis. We devise Emotion Extraction Layers (EEL) based on latest deep learning techniques like BERT (Bidirectional Encoder Representations from Transformers) and compare these models with traditional machine learning models. We show for a variety of emotions that the new deep learning models predict 1-5% (Sad, Fearful & Angry) better than traditional machine learning techniques. Further, we showcase that leveraging retail sentiment data using transfer learning techniques can help cross the cold start chasm of having no chatbot data initially, and this technique achieves -8% closer in performance when compared to having enough COVID sentiment data.

COVID, sentiment analysis, chatbots, BERT, deep learning, transfer learning