Feature Engineering for Twitter-based Applications

1.

[1]  Shervin Malmasi,et al.  Location Mention Detection in Tweets and Microblogs , 2015, PACLING.

[2]  Petra Kralj Novak,et al.  Sentiment of Emojis , 2015, PloS one.

[3]  Amit P. Sheth,et al.  Implicit Entity Linking in Tweets , 2016, ESWC.

[4]  Amit P. Sheth,et al.  Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models , 2017, COLING.

[5]  Ana-Maria Popescu,et al.  A Machine Learning Approach to Twitter User Classification , 2011, ICWSM.

[6]  Kyumin Lee,et al.  You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.

[7]  Amit Sheth,et al.  "Those edibles hit hard": Exploration of Twitter data on cannabis edibles in the U.S. , 2016, Drug and alcohol dependence.

[8]  Munmun De Choudhury,et al.  Quote RTs on Twitter: usage of the new feature for political discourse , 2016, WebSci.

[9]  Fernando Diaz,et al.  Emergency-relief coordination on social media: Automatically matching resource requests and offers , 2013, First Monday.

[10]  Ingmar Weber,et al.  U.S. Religious Landscape on Twitter , 2014, SocInfo.

[11]  Amit P. Sheth,et al.  Cursing in English on twitter , 2014, CSCW.

[12]  Iyad Rahwan,et al.  Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm , 2017, EMNLP.

[13]  Ari Rappoport,et al.  What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities , 2012, WSDM '12.

[14]  Taghi M. Khoshgoftaar,et al.  Impact of Feature Selection Techniques for Tweet Sentiment Classification , 2015, FLAIRS.

[15]  Gao Cong,et al.  Joint Recognition and Linking of Fine-Grained Locations from Tweets , 2016, WWW.

[16]  Mark Sanderson,et al.  Language Influences on Tweeter Geolocation , 2017, ECIR.

[17]  Amit P. Sheth,et al.  "Time for Dabs": Analyzing Twitter Data on Butane Hash Oil Use , 2015 .

[18]  Amit P. Sheth,et al.  Clustering for Simultaneous Extraction of Aspects and Features from Reviews , 2016, NAACL.

[19]  Amit P. Sheth,et al.  Citizen sensor data mining, social media analytics and development centric web applications , 2011, WWW.

[20]  Jürgen Pfeffer,et al.  Characterizing the life cycle of online news stories using social media reactions , 2013, CSCW.

[21]  Yair Neuman,et al.  Proactive screening for depression through metaphorical and automatic text analysis , 2012, Artif. Intell. Medicine.

[22]  Xiaojun Ma,et al.  Twitter User Gender Inference Using Combined Analysis of Text and Image Processing , 2014, VL@COLING.

[23]  Aron Culotta,et al.  Using matched samples to estimate the effects of exercise on mental health from twitter , 2015, AAAI 2015.

[24]  Paul Thompson,et al.  Predicting military and veteran suicide risk: Cultural aspects , 2014, CLPsych@ACL.

[25]  D. Ruths,et al.  What's in a Name? Using First Names as Features for Gender Inference in Twitter , 2013, AAAI Spring Symposium: Analyzing Microtext.

[26]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[27]  Judith Gelernter,et al.  Cross-lingual geo-parsing for non-structured data , 2013, GIR '13.

[28]  Amit P. Sheth,et al.  Are Twitter Users Equal in Predicting Elections? A Study of User Groups in Predicting 2012 U.S. Republican Presidential Primaries , 2012, SocInfo.

[29]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[30]  D. Lester,et al.  Twitter postings and suicide: An analysis of the postings of a fatal suicide in the 24 hours prior to death , 2015 .

[31]  Amit P. Sheth,et al.  EmojiNet: Building a Machine Readable Sense Inventory for Emoji , 2016, SocInfo.

[32]  Amit P. Sheth,et al.  Harnessing Twitter "Big Data" for Automatic Emotion Identification , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[33]  Amit P. Sheth,et al.  Implicit Entity Recognition in Clinical Documents , 2015, *SEMEVAL.

[34]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[35]  Amit P. Sheth,et al.  Extracting Diverse Sentiment Expressions with Target-Dependent Polarity from Twitter , 2012, ICWSM.

[36]  Krishnaprasad Thirunarayan,et al.  Knowledge will propel machine understanding of content: extrapolating from current examples , 2016, WI.

[37]  Roger C. Schank,et al.  Conceptual dependency: A theory of natural language understanding , 1972 .

[38]  Munmun De Choudhury,et al.  Gender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness , 2017, CSCW.

[39]  Krishnaprasad Thirunarayan,et al.  Extracting City Traffic Events from Social Streams , 2015, ACM Trans. Intell. Syst. Technol..

[40]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[41]  James Bailey,et al.  Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile? , 2009, PAKDD.

[42]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[43]  Lu Chen,et al.  Mining and Analyzing Subjective Experiences in User Generated Content , 2016 .

[44]  Glen Coppersmith,et al.  Exploratory Analysis of Social Media Prior to a Suicide Attempt , 2016, CLPsych@HLT-NAACL.

[45]  Ying Zhang,et al.  Retweet Modeling Using Conditional Random Fields , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[46]  Amit P. Sheth,et al.  Challenges of Sentiment Analysis for Dynamic Events , 2017, IEEE Intelligent Systems.

[47]  Stuart E. Middleton,et al.  Real-Time Crisis Mapping of Natural Disasters Using Social Media , 2014, IEEE Intelligent Systems.

[48]  Maarten Sap,et al.  The role of personality, age, and gender in tweeting about mental illness , 2015, CLPsych@HLT-NAACL.

[49]  Oren Etzioni,et al.  Named Entity Recognition in Tweets: An Experimental Study , 2011, EMNLP.

[50]  Mark Dredze,et al.  Shared Task : Depression and PTSD on Twitter , 2015 .

[51]  Dirk Hovy,et al.  Multitask Learning for Mental Health Conditions with Limited Social Media Data , 2017, EACL.

[52]  Amit P. Sheth,et al.  Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media , 2017, ASONAM.

[53]  Eric Horvitz,et al.  Predicting Depression via Social Media , 2013, ICWSM.

[54]  Li Sun,et al.  A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network , 2013, PAKDD Workshops.

[55]  A. Sheth,et al.  Discovering Fine-grained Sentiment in Suicide Notes , 2012, Biomedical informatics insights.

[56]  Brendan T. O'Connor,et al.  Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments , 2010, ACL.

[57]  Svetha Venkatesh,et al.  Affective and Content Analysis of Online Depression Communities , 2014, IEEE Transactions on Affective Computing.

[58]  Amit P. Sheth,et al.  EmojiNet: An Open Service and API for Emoji Sense Discovery , 2017, ICWSM.

[59]  Meeyoung Cha,et al.  Emoticon Style: Interpreting Differences in Emoticons Across Cultures , 2013, ICWSM.

[60]  Thomas Wetter,et al.  Screening Internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods , 2015, Comput. Methods Programs Biomed..

[61]  Amit P. Sheth,et al.  A Qualitative Examination of Topical Tweet and Retweet Practices , 2010, ICWSM.

[62]  Omar Alonso,et al.  User Taglines: Alternative Presentations of Expertise and Interest in Social Media , 2012, 2012 International Conference on Social Informatics.

[63]  Philip S. Yu,et al.  Mining Online Social Data for Detecting Social Network Mental Disorders , 2016, WWW.

[64]  S. Ye.  Measuring message propagation and social influence on Twitter , 2013 .

[65]  Amit P. Sheth,et al.  Finding street gang members on Twitter , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[66]  Michael J. Paul,et al.  Carmen: A Twitter Geolocation System with Applications to Public Health , 2013 .

[67]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[68]  Amit P. Sheth,et al.  A semantics-based measure of emoji similarity , 2017, WI.

[69]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[70]  Amit P. Sheth,et al.  Analyzing Clinical Depressive Symptoms in Twitter , 2016 .

[71]  Eric Horvitz,et al.  Social media as a measurement tool of depression in populations , 2013, WebSci.

[72]  Amit P. Sheth,et al.  Intent Classification of Short-Text on Social Media , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[73]  Georgi Georgiev,et al.  An Analysis of Event-Agnostic Features for Rumour Classification in Twitter , 2016, SMN@ICWSM.

[74]  Huan Liu,et al.  Twitter Data Analytics , 2013, SpringerBriefs in Computer Science.

[75]  Amit P. Sheth,et al.  Assisting coordination during crisis: a domain ontology based approach to infer resource needs from tweets , 2014, WebSci '14.

[76]  Mohamed Farouk Abdel Hady,et al.  Feature Selection for Twitter Sentiment Analysis: An Experimental Study , 2015, CICLing.

[77]  Derek Doran,et al.  Analyzing the social media footprint of street gangs , 2015, 2015 IEEE International Conference on Intelligence and Security Informatics (ISI).

[78]  Timothy Baldwin,et al.  pigeo: A Python Geotagging Tool , 2016, ACL.

[79]  Beth Levin,et al.  English Verb Classes and Alternations: A Preliminary Investigation , 1993 .

[80]  Amit P. Sheth,et al.  Twitris: A System for Collective Social Intelligence , 2014, Encyclopedia of Social Network Analysis and Mining.

[81]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[82]  Kalina Bontcheva,et al.  TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text , 2013, RANLP.

[83]  Amit P. Sheth,et al.  Finding Influential Authors in Brand-Page Communities , 2012, ICWSM.

[84]  Christopher D. Manning,et al.  Baselines and Bigrams: Simple, Good Sentiment and Topic Classification , 2012, ACL.

[85]  Ian D. Wood,et al.  Emoji as Emotion Tags for Tweets , 2016 .

[86]  Amit P. Sheth,et al.  Knowledge Enabled Approach to Predict the Location of Twitter Users , 2015, ESWC.

[87]  Michael D. Barnes,et al.  Tracking suicide risk factors through Twitter in the US. , 2014, Crisis.

[88]  David W. McDonald,et al.  Perception Differences between the Depressed and Non-Depressed Users in Twitter , 2013, ICWSM.

[89]  Amit P. Sheth,et al.  Word Embeddings to Enhance Twitter Gang Member Profile Identification , 2016, ArXiv.

[90]  Raphaël Troncy,et al.  Analysis of named entity recognition and linking for tweets , 2014, Inf. Process. Manag..