Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
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
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Generating a non-English subjectivity lexicon: relations that matter
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Cross language text categorization by acquiring multilingual domain models from comparable corpora
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Exploiting emoticons in sentiment analysis
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Sentiment analysis refers to retrieving an author's sentiment from a text. We analyze the differences that occur in sentiment scoring across languages. We present our experiments for the Dutch and English language based on forum, blog, news and social media texts available on the Web, where we focus on the differences in the use of a language and the effect of the grammar of a language on sentiment analysis. We propose a multilingual pipeline for evaluating how an author's sentiment is conveyed in different languages. We succeed in correctly classifying positive and negative texts with an accuracy of approximately 71% for English and 79% for Dutch. The evaluation of the results shows however that usage of common expressions, emoticons, slang language, irony, sarcasm, and cynicism, acronyms and different ways of negation in English prevent the underlying sentiment scores from being directly comparable.