Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Opinion identification in Spanish texts
YIWCALA '10 Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas
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Opinion mining deals with determining of the sentiment orientation--positive, negative, or neutral--of a (short) text. Recently, it has attracted great interest both in academia and in industry due to its useful potential applications. One of the most promising applications is analysis of opinions in social networks. In this paper, we examine how classifiers work while doing opinion mining over Spanish Twitter data. We explore how different settings (n-gram size, corpus size, number of sentiment classes, balanced vs. unbalanced corpus, various domains) affect precision of the machine learning algorithms. We experimented with Naïve Bayes, Decision Tree, and Support Vector Machines. We describe also language specific preprocessing--in our case, for Spanish language--of tweets. The paper presents best settings of parameters for practical applications of opinion mining in Spanish Twitter. We also present a novel resource for analysis of emotions in texts: a dictionary marked with probabilities to express one of the six basic emotions(Probability Factor of Affective use (PFA)(Spanish Emotion Lexicon that contains 2,036 words.