TY - GEN
T1 - Harnessing the power of hashtags in tweet analytics
AU - Gupta, Vibhuti
AU - Hewett, Rattikorn
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Twitter is one of the most popular microblogging platforms where users can interact with each other by posting texts of up to 140 characters called tweets. Because of the large and fast growing number of tweets being generated daily, tweet analytics is viewed as one of the fundamental problems of Big data stream. Recently, hashtags, hyperlinked words, in tweets have been applied for tweet retrieval, trend/event detection and advertisement. However, using hashtags for tweet classification remains challenging because we have to cope with context dependent words, slangs, abbreviations, and emoticons with a limited small number of words and an evolving use of hashtags. Most existing approaches deal with classifying tweet sentiments by using the lexicon and meaning of hashtags. Our research aims to classify tweets by topics. Unlike sentiment analytics, hashtags for describing a topic need to be more diverse to cover various aspects of a topic. This paper presents a tweet analytics approach that uses domain-specific knowledge to create a set of strong hashtag predictors for tweet topic classification. The paper describes the approach and preliminary experiments that show promising results toward Big data tweet analytics.
AB - Twitter is one of the most popular microblogging platforms where users can interact with each other by posting texts of up to 140 characters called tweets. Because of the large and fast growing number of tweets being generated daily, tweet analytics is viewed as one of the fundamental problems of Big data stream. Recently, hashtags, hyperlinked words, in tweets have been applied for tweet retrieval, trend/event detection and advertisement. However, using hashtags for tweet classification remains challenging because we have to cope with context dependent words, slangs, abbreviations, and emoticons with a limited small number of words and an evolving use of hashtags. Most existing approaches deal with classifying tweet sentiments by using the lexicon and meaning of hashtags. Our research aims to classify tweets by topics. Unlike sentiment analytics, hashtags for describing a topic need to be more diverse to cover various aspects of a topic. This paper presents a tweet analytics approach that uses domain-specific knowledge to create a set of strong hashtag predictors for tweet topic classification. The paper describes the approach and preliminary experiments that show promising results toward Big data tweet analytics.
KW - Big Data Stream
KW - Hashtags
KW - Ontology
KW - Social Media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85047825216&partnerID=8YFLogxK
U2 - 10.1109/BigData.2017.8258194
DO - 10.1109/BigData.2017.8258194
M3 - Conference contribution
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 2390
EP - 2395
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2017 through 14 December 2017
ER -