This paper presents the SWATCS65 ensemble classifier used to identify the sentiment of tweets. The classifier was trained and tested using data provided by Semeval-2015, Task 10, subtask B with the goal to label the sentiment of an entire tweet. The ensemble was constructed from 26 classifiers, each written by a group of one to three undergraduate students in the Fall 2014 offering of a natural language processing course at Swarthmore College. Each of the classifiers was designed independently, though much of the early structure was provided by in-class lab assignments. There was high variability in the final performance of each of these classifiers, which were combined using a weighted voting scheme with weights correlated with performance using 5-fold cross-validation on the provided training data. The system performed very well, achieving an F1 score of 61.89.
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