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De Choudhury, Munmun

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    JobLex: A Lexico-Semantic Knowledgebase of Occupational Information Descriptors
    (Georgia Institute of Technology, 2019-07) Saha, Koustuv ; Reddy, Manikanta D. ; De Choudhury, Munmun
    Technological advancements in several work sectors have influenced evolution of the landscape of work at an unprecedented speed, leading to the demand of continuous skill development [1,8]. In turn, this interests a number of stakeholders spanning across academia and industry in a number of disciplines including labor economics, who leverage large-scale data available from a variety of offline and online sources (e.g., resumes, job portals, professional social networking such as LinkedIn, search engine, job databases, etc.) [9,11,12]. On these data streams, describing job aspects and skills vary extensively, confounded by factors such as self-presentation, subjective perspectives on soft and hard skills, audience, and intrinsic traits such as personality and mindset [2,4,7,15,17]. Such data analyses require a taxonomy of keywords that are associated with skills per job description or type. However, most databases are only limited — they do not capture variants, typos, abbreviations, or internet slangs that are used on social media or in informal settings [6]. To facilitate research in this space, our work builds on a well-validated dictionary of occupational descriptors (O*Net) to propose a method, and correspondingly a knowledgebase, JobLex of occupational descriptors that can be used in computational social science and organizational studies [13]. We publish both our script and an example lexicon (for Twitter) for purposes of research and practical application. Item Description: This work publishes a methodology to build knowledgebase of occupational information descriptors. We also provide access to the codebase and example lexicon.
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    Towards Using Visual Attributes to Infer Image Sentiment Of Social Events
    (Georgia Institute of Technology, 2017) Ahsan, Unaiza ; De Choudhury, Munmun ; Essa, Irfan
    Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.