Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Data mining with neural networks and support vector machines using the R/rminer tool
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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The analysis of microblogging data related with stock markets can reveal relevant new signals of investor sentiment and attention. It may also provide sentiment and attention indicators in a more rapid and cost-effective manner than other sources. In this study, we created several indicators using Twitter data and investigated their value when modeling relevant stock market variables, namely returns, trading volume and volatility. We collected recent data from nine major technological companies. Several sentiment analysis methods were explored, by comparing 5 popular lexical resources and two novel lexicons (emoticon based and the merge of all 6 lexicons) and sentiment indicators produced using two strategies (based on daily words and individual tweet classifications). Also, we measured posting volume associated with tweets related to the analyzed companies. While a short time period is considered (32 days), we found scarce evidence that sentiment indicators can explain these stock returns. However, interesting results were obtained when measuring the value of using posting volume for fitting trading volume and, in particular, volatility.