The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyse text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist.

[1]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[2]  Fernando Diaz,et al.  CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises , 2014, ICWSM.

[3]  Ellen Riloff,et al.  Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.

[4]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[5]  Fabrício Benevenuto,et al.  Comparing and combining sentiment analysis methods , 2013, COSN '13.

[6]  Jonathon Read,et al.  Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.

[7]  Marie-Francine Moens,et al.  Model-Portability Experiments for Textual Temporal Analysis , 2011, ACL.

[8]  James Pustejovsky,et al.  SemEval-2007 Task 15: TempEval Temporal Relation Identification , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[9]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[10]  Lina Zhou,et al.  Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[11]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[12]  Tom M. Mitchell,et al.  Coupled temporal scoping of relational facts , 2012, WSDM '12.

[13]  Philip J. Stone,et al.  A computer approach to content analysis: studies using the General Inquirer system , 1963, AFIPS Spring Joint Computing Conference.

[14]  Irina P. Temnikova,et al.  EMTerms 1.0: A Terminological Resource for Crisis Tweets , 2015, ISCRAM.

[15]  Marie-Francine Moens,et al.  Automatic Sentiment Analysis in On-line Text , 2007, ELPUB.

[16]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[17]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[18]  Andrea Esuli,et al.  SentiWordNet: A High-Coverage Lexical Resource for Opinion Mining , 2006 .

[19]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[20]  Inderjeet Mani,et al.  Robust Temporal Processing of News , 2000, ACL.

[21]  Shan Wang,et al.  Classifying Temporal Relations Between Events , 2007, ACL.

[22]  Michael Gertz,et al.  Multilingual and cross-domain temporal tagging , 2012, Language Resources and Evaluation.

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

[24]  Carolyn Penstein Rosé,et al.  Extracting Events with Informal Temporal References in Personal Histories in Online Communities , 2013, ACL.

[25]  Michael Gertz,et al.  Temporal Tagging on Different Domains: Challenges, Strategies, and Gold Standards , 2012, LREC.

[26]  John Carroll,et al.  Weakly supervised techniques for domain-independent sentiment classification , 2009, TSA@CIKM.

[27]  Qi Deng,et al.  Building an Environmental Sustainability Dictionary for the IT Industry , 2017, HICSS.

[28]  James Pustejovsky,et al.  Temporal and Event Information in Natural Language Text , 2005, Lang. Resour. Evaluation.

[29]  Michael Gertz,et al.  HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal Expressions , 2010, *SEMEVAL.

[30]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[31]  Ghazaleh Beigi,et al.  An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief , 2016, Sentiment Analysis and Ontology Engineering.

[32]  Zhoujun Li,et al.  Exploiting Timelines to Enhance Multi-document Summarization , 2014, ACL.

[33]  M. Bradley,et al.  Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings , 1999 .

[34]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[35]  Michael Gertz,et al.  Temporal Information Retrieval , 2009, Encyclopedia of Database Systems.

[36]  Angel X. Chang,et al.  SUTime: A library for recognizing and normalizing time expressions , 2012, LREC.

[37]  Michael Gertz,et al.  On the value of temporal information in information retrieval , 2007, SIGF.

[38]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[39]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[40]  Frank Schilder,et al.  From Temporal Expressions To Temporal Information: Semantic Tagging Of News Messages , 2001, The Language of Time - A Reader.

[41]  Mike Thelwall,et al.  Information-centered research for large-scale analyses of new information sources , 2008, J. Assoc. Inf. Sci. Technol..

[42]  Inderjeet Mani,et al.  2003 Standard for the Annotation of Temporal Expressions , 2004 .

[43]  Tommaso Caselli,et al.  SemEval-2010 Task 13: TempEval-2 , 2010, *SEMEVAL.

[44]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[45]  Mahmoud Al-Ayyoub,et al.  Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis , 2014, Int. J. Inf. Technol. Web Eng..

[46]  James Pustejovsky,et al.  TimeML: Robust Specification of Event and Temporal Expressions in Text , 2003, New Directions in Question Answering.

[47]  Hsinchun Chen,et al.  Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums , 2008, TOIS.

[48]  James Pustejovsky,et al.  TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations , 2012, ArXiv.

[49]  Maarten Sap,et al.  Extracting Human Temporal Orientation from Facebook Language , 2015, NAACL.

[50]  Abdul Razak Hamdan,et al.  Sentiment analysis techniques in recent works , 2015, 2015 Science and Information Conference (SAI).

[51]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .

[52]  Chih-Ping Wei,et al.  Understanding Online Consumer Review Opinions with Sentiment Analysis using Machine Learning , 2010, Pac. Asia J. Assoc. Inf. Syst..