Related references
Note: Only part of the references are listed.Exploratory Geovisualization of the Character and Distribution of American Climate Change Beliefs
Johnathan W. Sugg
WEATHER CLIMATE AND SOCIETY (2021)
A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change
May El Barachi et al.
JOURNAL OF CLEANER PRODUCTION (2021)
Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets
Moloud Abdar et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)
Sensing climate change and energy issues: Sentiment and emotion analysis with social media in the UK and Spain
Maria L. Loureiro et al.
ENERGY POLICY (2020)
The Berkeley Earth Land/Ocean Temperature Record
Robert A. Rohde et al.
EARTH SYSTEM SCIENCE DATA (2020)
Temperature and temperament: Evidence from Twitter
Patrick Baylis
JOURNAL OF PUBLIC ECONOMICS (2020)
Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma
Muhammed Ali Sit et al.
INTERNATIONAL JOURNAL OF DIGITAL EARTH (2019)
Topic modeling and sentiment analysis of global climate change tweets
Biraj Dahal et al.
SOCIAL NETWORK ANALYSIS AND MINING (2019)
Credibility of climate change denial in social media
Abhishek Samantray et al.
PALGRAVE COMMUNICATIONS (2019)
Detecting Climate Change Deniers on Twitter Using a Deep Neural Network
Xingyu Chen et al.
ICMLC 2019: 2019 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING (2019)
A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis
Symeon Symeonidis et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation
David Zimbra et al.
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS (2018)
Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts
Anne Cocos et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2017)
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings
Chenliang Li et al.
ACM TRANSACTIONS ON INFORMATION SYSTEMS (2017)
Social Media, Science, and Attack Discourse: How Twitter Discussions of Climate Change Use Sarcasm and Incivility
Ashley A. Anderson et al.
SCIENCE COMMUNICATION (2017)
When do extreme weather events generate attention to climate change?
Matthew R. Sisco et al.
CLIMATIC CHANGE (2017)
Word network topic model: a simple but general solution for short and imbalanced texts
Yuan Zuo et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2016)
Like It or Not: A Survey of Twitter Sentiment Analysis Methods
Anastasia Giachanou et al.
ACM COMPUTING SURVEYS (2016)
Rapid assessment of disaster damage using social media activity
Yury Kryvasheyeu et al.
SCIENCE ADVANCES (2016)
People as sensors: Mass media and local temperature influence climate change discussion on Twitter
Andrei P. Kirilenko et al.
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS (2015)
Network analysis reveals open forums and echo chambers in social media discussions of climate change
Hywel T. P. Williams et al.
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS (2015)
Gender differences in the climate change communication on Twitter
Kim Holmberg et al.
INTERNET RESEARCH (2015)
Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll
Emily M. Cody et al.
PLOS ONE (2015)
Efficient Machine Learning for Big Data: A Review
Omar Y. Al-Jarrah et al.
BIG DATA RESEARCH (2015)
Public microblogging on climate change: One year of Twitter worldwide
Andrei P. Kirilenko et al.
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS (2014)
BTM: Topic Modeling over Short Texts
Xueqi Cheng et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)
Where in the World Are You? Geolocation and Language Identification in Twitter
Mark Graham et al.
PROFESSIONAL GEOGRAPHER (2014)
Shifting public opinion on climate change: an empirical assessment of factors influencing concern over climate change in the U.S., 2002-2010
Robert J. Brulle et al.
CLIMATIC CHANGE (2012)
Text classification from labeled and unlabeled documents using EM
K Nigam et al.
MACHINE LEARNING (2000)
Share Paper
