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This paper describes our systems for expression-level and message-level sentiment analysis ‐ two subtasks of SemEval-2015 Task 10 on sentiment analysis in Twitter. First we built two baseline systems for the two subtasks using SVM with a variety of features. Then we improved the systems through model iteration and probability-output weighting respectively. Our submissions are ranked the 3rd and 2nd among eleven teams on the 2015 test set and progress test set in subtask A and the 7th and 4th among 40 teams on the two test sets respectively in subtask B.

IOA: Improving SVM Based Sentiment Classification Through Post Processing