EmoGram: An open-source time sequence-based emotion tracker and its innovative applications

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

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    3 Citations (Scopus)


    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.

    Original languageEnglish
    Title of host publicationThe Workshops of the Thirtieth AAAI Conference on Artificial Intelligence
    Place of PublicationPalo Alto, California
    PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
    Number of pages5
    ISBN (Electronic)9781577357599
    Publication statusPublished - 2016
    EventAAAI Conference on Artificial Intelligence Workshops 2016: Knowledge Extraction from Text - Phoenix, United States of America
    Duration: 12 Feb 201613 Feb 2016
    Conference number: 30th


    ConferenceAAAI Conference on Artificial Intelligence Workshops 2016
    CountryUnited States of America

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