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Lexichrome: Lexical Discovery with Word-Color Associations


Chris K. Kim, Christopher Collins, Uta Hinrichs, Saif M. Mohammad

Based on word-colour associations from a comprehensive, crowdsourced lexicon, we present Lexichrome: a web application that explores the popular perception of relationships between English words and eleven basic colour terms using interactive visualization. Lexichrome provides three complementary visualizations: “Palette” presents the diversity of word-colour associations across the colour palette; “Words” reveals the colour associations of individual words using a dictionary-like interface; “Roget’s Thesaurus” uncovers colour association patterns in different semantic categories found in the thesaurus. Finally, our text editor allows users to compose their own texts and examine the resultant chromatic fingerprints throughout the process. We studied the utility of Lexichrome in a two-part qualitative user study with nine participants from various writing-intensive professions. We find that the presence of word-colour associations promotes awareness surrounding word choice, editorial decision, and audience reception, and introduces a variety of use cases, features, and future opportunities applicable to creative writing, corporate communication, and journalism.

Lexichrome is available for public access at



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Thanks to Jason Boyd and Laurie Petrou. This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).

SentimentState: Exploring Sentiment Analysis on Twitter

Twitter feeds are a potential source of useful information regarding the state of mind of persons who are the subject of legal or medical assessment. These may include persons suspected of committing crimes or patients that arrive at a hospital for a mental health emergency, for example, attempted suicide. Messages called “tweets” can expose the state of mind of a Twitter user.  Analysts are challenged with creating reports of the online presence of users quickly and efficiently. We present a web-based visualization tool called SentimentState that performs sentiment analysis on tweets from a user’s Twitter account.

SentimentState analyses tweets based on ten emotions (positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise and trust) and creates an interactive timeline graph of the emotional state of the user. It uses a collection of emotion 24,200 word-sense pairs collected from the National Research Council of Canada (NRC). We anticipate that this interactive visualization can have applications throughout, and even beyond, legal and medical assessments, and will provide analysts with timely and relevant information regarding the mood state of clients, patients and other persons under assessment.

Check out our Online Demo and our GitHub Repository for source code related to this project.


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Thanks to Saif Mohammed for providing the NRC Emotion Lexicon for this project.