EmoGram: An Open-Source Time Sequence-Based Emotion Tracker and Its Innovative Applications

Aditya Joshi, Vaibhav Tripathi, Ravindra Soni, Pushpak Bhattacharyya, Mark James Carman

Abstract: In this paper, we present an open-source emotion tracker and its innovative applications. Our tracker, EmoGram, tracks emotion changes for a sequence of textual units. It is versatile in terms of the textual unit (tweets, sentences in discourse, etc.) and also what constitutes the time sequence (timestamps of tweets, discourse nature of text, etc.). We demonstrate the utility of our system through our applications: a sequence of commentaries in cricket matches, a sequence of dialogues in a play, and a sequence of tweets related to the Maggi controversy in India in 2015. That one system can be used for these applications is the merit of EmoGram.