EmojiNet: An Open Service and API for Emoji Sense Discovery

Slides:

Advertisements

Similar presentations

HATHI TRUST A Shared Digital Repository Delivering Data For New Generations of Research Strategies and Challenges Jeremy York NISO/BISG Forum ALA 2010.

Advertisements

Semantic Annotation and Search for Resources in the Next Generation Web Ajith H. Ranabahu, Amit Sheth, Maryam Panahiazar, Sanjaya Wijeratne Kno.e.sis Center.

RDB2RDF: Incorporating Domain Semantics in Structured Data Satya S. Sahoo Kno.e.sis CenterKno.e.sis Center, Computer Science and Engineering Department,

Linking Entities in #Microposts ROMIL BANSAL, SANDEEP PANEM, PRIYA RADHAKRISHNAN, MANISH GUPTA, VASUDEVA VARMA INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY,

TAILS: COBWEB 1 [1] Online Digital Learning Environment for Conceptual Clustering This material is based upon work supported by the National Science Foundation.

One Theme in All Views: Modeling Consensus Topics in Multiple Contexts Jian Tang 1, Ming Zhang 1, Qiaozhu Mei 2 1 School of EECS, Peking University 2 School.

Title Course opinion mining methodology for knowledge discovery, based on web social media Authors Sotirios Kontogiannis Ioannis Kazanidis Stavros Valsamidis.

GENERATING AUTOMATIC SEMANTIC ANNOTATIONS FOR RESEARCH DATASETS AYUSH SINGHAL AND JAIDEEP SRIVASTAVA CS DEPT., UNIVERSITY OF MINNESOTA, MN, USA.

Linked Sensor Data Harshal Patni, Cory Henson, Amit P. Sheth Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University,

Analyzing the Social Media Footprint of Street Gangs 1 Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)Kno.e.sis 2 Center for Urban.

Semantic Search Jiawei Rong Authors Semantic Search, in Proc. Of WWW Author R. Guhua (IBM) Rob McCool (Stanford University) Eric Miller.

Gimme’ The Context: Context- driven Automatic Semantic Annotation with CPANKOW Philipp Cimiano et al.

ECDL 2002 Employing Smart Browsers to Support Flexible Information Presentation in Petri net-based Digital Libraries Unmil P. Karadkar, Richard Furuta.

The Social Web: A laboratory for studying s ocial networks, tagging and beyond Kristina Lerman USC Information Sciences Institute.

Knowledge Science & Engineering Institute, Beijing Normal University, Analyzing Transcripts of Online Asynchronous.

Integrating Complementary Tools with PopMedNet TM 27 July 2015 Rich Schaaf

Automated Tracking of Online Service Policies J. Trent Adams 1 Kevin Bauer 2 Asa Hardcastle 3 Dirk Grunwald 2 Douglas Sicker 2 1 The Internet Society 2.

A Statistical and Schema Independent Approach to Identify Equivalent Properties on Linked Data † Kno.e.sis Center Wright State University Dayton OH, USA.

Copyright © 2010 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Multiple Ontologies in.

Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection Boanerges Aleman-Meza, Meenakshi Nagarajan,

Printing: This poster is 48” wide by 36” high. It’s designed to be printed on a large-format printer. Customizing the Content: The placeholders in this.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification on Reviews Peter D. Turney Institute for Information Technology National.

This material is based upon work supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs,

Krishnaprasad Thirunarayan, Pramod Anantharam, Cory A. Henson, and Amit P. Sheth Kno.e.sis Center, Ohio Center of Excellence on Knowledge-enabled Computing,

Name : Emad Zargoun Id number : EASTERN MEDITERRANEAN UNIVERSITY DEPARTMENT OF Computing and technology “ITEC547- text mining“ Prof.Dr. Nazife Dimiriler.

SWETO: Large-Scale Semantic Web Test-bed Ontology In Action Workshop (Banff Alberta, Canada June 21 st 2004) Boanerges Aleman-MezaBoanerges Aleman-Meza,

Microsoft Academic Search Search | Explore | Discover Alex D. Wade Director - Scholarly Communication.

1 Information Retrieval Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow)

Paper Review by Utsav Sinha August, 2015 Part of assignment in CS 671: Natural Language Processing, IIT Kanpur.

Temporal Analysis using Sci2 Ted Polley and Dr. Katy Börner Cyberinfrastructure for Network Science Center Information Visualization Laboratory School.

University of Florida CTSI: Consuming and disambiguating publications data from Microsoft Academic Search in VIVO. Nicholas Rejack 1, Erik Schmidt 1, Michael.

Relevance Detection Approach to Gene Annotation Aid to automatic annotation of databases Annotation flow –Extraction of molecular function of a gene from.

Wikipedia as Sense Inventory to Improve Diversity in Web Search Results Celina SantamariaJulio GonzaloJavier Artiles nlp.uned.es UNED,c/Juan del Rosal,

What Is Text Mining? Also known as Text Data Mining Process of examining large collections of unstructured textual resources in order to generate new.

1 A Biterm Topic Model for Short Texts Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng Institute of Computing Technology, Chinese Academy of Sciences.

Geographical Latent Variable Models for Microblog Retrieval Alexander Kotov 1,2 Vineeth Rakesh 2 Eugene Agichtein 3 Chandan K. Reddy 2 1 Textual Data Analytics.

Overview of Statistical NLP IR Group Meeting March 7, 2006.

Selected Semantic Web UMBC CoBrA – Context Broker Architecture  Using OWL to define ontologies for context modeling and reasoning  Taking.

Semantic Web Technologies Readings discussion Research presentations Projects & Papers discussions.

Word Embeddings to Enhance Twitter Gang Member Profile Identification Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)Kno.e.sis Wright.

Text and Data Mining for Systematic Reviews Investigating Trends to Update Collaboration Services Virginia Pannabecker Virginia Tech, University Libraries.

Microsoft Academic Search Search | Explore | Discover

Finding Street Gang Members on Twitter

EmojiNet: A Machine Readable Emoji Sense Inventory

Introduction to gathering and analyzing data via APIs Gus Cavanaugh

Finding funding Fall 2015.

Signals Revealing Street Gang Members on Twitter

EmojiNet: Building a Machine Readable Sense Inventory for Emoji

By : Namesh Kher Big Data Insights – INFM 750

Mobile App Trends: lifecycle, functions, and cognitive

Cloud computing-The Future Technologies

Context-Specific Intention Awareness through Web Query

GS/PPAL Research Methods and Information Systems

Detecting Cyberbullying using Latent Semantic Indexing(LSI)

Natural Language Processing (NLP)

Summary Presented by : Aishwarya Deep Shukla

Knowledge Discovery in the Semantic Web

A Semantics-Based Measure of

Collective Network Linkage across Heterogeneous Social Platforms

WebAnywhere Addressing Performance and Security

N. Capp, E. Krome, I. Obeid and J. Picone

Quanzeng You, Jiebo Luo, Hailin Jin and Jianchao Yang

Smart Portal To Protect Child Online

Learning Emoji Embeddings Using Emoji Co-Occurrence Network Graph

Disambiguation Algorithm for People Search on the Web

How to publish in a format that enhances literature-based discovery?

Course Introduction CSC 576: Data Mining.

Natural Language Processing (NLP)

Microsoft Azure Data Catalog

Natural Language Processing (NLP)

Presentation transcript:

EmojiNet: An Open Service and API for Emoji Sense Discovery Presented at the 11th International AAAI Conference on Web and Social Media (ICWSM 2017) Montreal, Canada, 15th – 18th May, 2017 Sanjaya Wijeratne sanjaya@knoesis.org Lakshika Balasuriya lakshika@knoesis.org Amit Sheth amit@knoesis.org Derek Doran derek@knoesis.org Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University, Dayton, OH, USA

What does this emoji mean Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Source – https://youtu.be/5JdVsbNqYno What does this emoji mean Source – https://youtu.be/5JdVsbNqYno Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Source – https://youtu.be/5JdVsbNqYno What does this emoji mean Source – https://youtu.be/5JdVsbNqYno Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

What does this emoji mean U+1F481 INFORMATION DESK PERSON Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

What does this emoji mean Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Source – https://youtu.be/5JdVsbNqYno What does this emoji mean Source – https://youtu.be/5JdVsbNqYno Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Source – https://media.giphy.com/media/BNKGM6uOgPhp6/giphy.gif What does this emoji mean Source – https://media.giphy.com/media/BNKGM6uOgPhp6/giphy.gif Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

What does this emoji mean U+1F450 OPEN HANDS SIGN Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

What is EmojiNet A dataset of emoji meanings and interpretations Almost 13,000 senses: a word(pos_tag)pair conveying notional interpretations of an emoji and the part of speech tag of the interpretations Includes many possible senses per emoji Agglomerates data across open web resources, connects sense labels to sense definitions by WSD task supported by embedding models to BabelNet Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Source – https://goo.gl/rjS1hX Why EmojiNet Emoji is ambiguous – often, emoji are filtered, or otherwise not considered in NLP tasks on CMC EmojiNet offers a source of emoji meanings and context towards emoji disambiguation Goal: Support NLP, social media, CMC work trying to extract meaning from emoji-laden (short) text Source – https://goo.gl/rjS1hX Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Building EmojiNet Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

EmojiNet Sense Queries Emoji Sense Distribution Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

EmojiNet Sense Queries icing(noun) pudding(noun) stink(verb) crappy(adjective) Emoji Sense Distribution Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

EmojiNet Sense Queries cutie(noun) infatuation(noun) hug(verb) flirty(adjective) Emoji Sense Distribution Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

EmojiNet Sense Queries fashion(noun) girl(noun) wearing(verb) female(adjective) Emoji Sense Distribution Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Example Application – Emoji Similarity Emoji Pair Similarity 0.60 0.57 Emoji Similarity – Similar Emoji Form Clusters Jaccard Similarity of Emoji Based on Emoji Sense Labels Emoji Pair Similarity 0.60 0.57 0.56 0.52 0.50 0.48 0.47 Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

EmojiNet Dataset and REST API The dataset is available for use/download in many formats Browse the Dataset – http://emojinet.knoesis.org/ Download as flat files, Platform-specific emoji meanings, Emoji Similarity Datasets available at – http://emojinet.knoesis.org/datasets.php Programmatically Access EmojiNet via a REST API, Documentation, Sample API Requests and JSON Responses available at – http://emojinet.knoesis.org/api.php Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Applications of EmojiNet – Emoji Sense Disambiguation We selected 25 most commonly misunderstood emoji and selected 50 tweets for each emoji Used Simplified LESK algorithm for disambiguation Context words were learned for each emoji sense definition using Twitter and Google News-based word embedding models Twitter-based embeddings outperform others Top 10 Emoji based on the Emoji Sense Disambiguation Accuracy (in % values) Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.

Thank You! Visit us at http://emojinet.knoesis.org/ derek@knoesis.org http://knoesis.org/people/derek/ Wijeratne, Sanjaya et al. Word Embeddings to Enhance Twitter Gang Member Profile Identification SML @ IJCAI 2016

Acknowledgement We are grateful to Nicole Selken, the designer of The Emoji Dictionary and Jeremy Burge, the founder of Emojipedia for giving us the permission to use their web resources for our research. We are thankful to Scott Duberstein for helping us with setting up Amazon Mechanical Turk tasks. We acknowledge partial support from the National Science Foundation (NSF) award: CNS-1513721: “Context-Aware Harassment Detection on Social Media”, the National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454- 02: “Trending: Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid Use” and the National Institutes of Mental Health (NIMH) award: 1R01MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression”. Points of view or opinions in this document are those of the authors and do not necessarily represent the official position or policies of the NSF, NIDA, or NIMH. Wijeratne, Sanjaya et al. EmojiNet: An Open Service and API for Emoji Sense Discovery, ICWSM 2017.