INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic generation of a training corpus for unsupervised identification of stance in tweets

SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

Volumn , Issue , 2016, Pages 378-383

  Dias, Marcelo ^a   Becker, Karin ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

SEMANTICS;

AUTOMATIC GENERATION; HASHTAGS; N-GRAMS; OPINION MINING; OPINION TARGETS; PREDICTION MODEL; TRAINING CORPUS;

SENTIMENT ANALYSIS;

EID: 85013069126     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/s16-1061     Document Type: Conference Paper

References (5)

1. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. 2009. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10-18.
2. BingLiu. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1-167.
3. Saif M. Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. SemEval-2016 task 6: Detecting stance in tweets. In Proceedings of the International Workshop on Semantic Evaluation, SemEval'16, San Diego, California, June.
4. Saif M. Mohammad. 2016. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Herb Meiselman, editor, Emotion Measurement. Elsevier.
5. Marilyn A Walker, Pranav Anand, Rob Abbott, Jean E Fox Tree, Craig Martell, and Joseph King. 2012. That is your evidence?: Classifying stance in online political debate. Decision Support Systems, 53(4):719-729.