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- W2182360139 abstract "This paper describes our automatic sentiment analysis system ‐ CIS-positive ‐ for SemEval 2015 Task 10 “Sentiment Analysis in Twitter”, subtask B “Message Polarity Classification”. In this system, we propose to normalize the Twitter data in a way that maximizes the coverage of sentiment lexicons and minimizes distracting elements. Furthermore, we integrate the output of Convolutional Neural Networks into Support Vector Machines for the polarity classification. Our system achieves a macro F1 score of the positive and negative class of 59.57 on the SemEval 2015 test data." @default.
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- W2182360139 date "2015-01-01" @default.
- W2182360139 modified "2023-09-30" @default.
- W2182360139 title "CIS-positive: Combining Convolutional Neural Networks and SVMs for Sentiment Analysis in Twitter" @default.
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