no code implementations • 26 Feb 2018 • Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth
In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment. We crawled data from Twitter using our offensive lexicon.
2 code implementations • 14 Jul 2017 • Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base.
no code implementations • 14 Jul 2017 • Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web.
no code implementations • 29 Oct 2016 • Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth
A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population.
no code implementations • 27 Oct 2016 • Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly.
no code implementations • 25 Oct 2016 • Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date.