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TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter.

, , , , , , , , and . IJCNLP (System Demonstrations), page 21-24. Association for Computational Linguistics, (2017)

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Towards Real-Time, Country-Level Location Classification of Worldwide Tweets., , , , , and . IEEE Trans. Knowl. Data Eng., 29 (9): 2053-2066 (2017)Combining Heterogeneous User Generated Data to Sense Well-being., , , , , and . COLING, page 3007-3018. ACL, (2016)Building and evaluating resources for sentiment analysis in the Greek language., , , , , , , and . Language Resources and Evaluation, 52 (4): 1021-1044 (2018)Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum., , , and . CIKM, page 367-376. ACM, (2018)TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter., , , , , , , , and . IJCNLP (System Demonstrations), page 21-24. Association for Computational Linguistics, (2017)An Ensemble Model for Cross-Domain Polarity Classification on Twitter., , and . WISE (2), volume 8787 of Lecture Notes in Computer Science, page 168-177. Springer, (2014)WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition., , , , , , , and . SemEval@NAACL-HLT, page 657-663. The Association for Computer Linguistics, (2015)Predicting Elections for Multiple Countries Using Twitter and Polls., , , and . IEEE Intelligent Systems, 30 (2): 10-17 (2015)Can We Assess Mental Health Through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation., , , and . ECML/PKDD (3), volume 11053 of Lecture Notes in Computer Science, page 407-423. Springer, (2018)Towards Real-Time, Country-Level Location Classification of Worldwide Tweets., , , , , and . CoRR, (2016)