Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Multimodal subjectivity analysis of multiparty conversation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Efficient handling of N-gram language models for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Lexicon-based methods for sentiment analysis
Computational Linguistics
KenLM: faster and smaller language model queries
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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The classification of opinion texts in positive and negative can be tackled by evaluating separate key words but this is a very limited approach. We propose an approach based on the order of the words without using any syntactic and semantic information. It consists of building one probabilistic model for the positive and another one for the negative opinions. Then the test opinions are compared to both models and a decision and confidence measure are calculated. In order to reduce the complexity of the training corpus we first lemmatize the texts and we replace most named-entities with wildcards. We present an accuracy above 81% for Spanish opinions in the financial products domain.