Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) 2015
DOI: 10.18653/v1/s15-2106
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SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression

Abstract: This paper describes SWATAC, a system built for SemEval-2015's Task 10 Subtask B, namely the Message Polarity Classification Task. Given a tweet, the system classifies the sentiment as either positive, negative, or neutral. Several preprocessing tasks such as negation detection, spell checking, and tokenization are performed to enhance lexical information. The features are then augmented with external sentiment lexicons. Classification is done with Logistic Regression using a one-vsrest configuration. For the … Show more

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Cited by 6 publications
(2 citation statements)
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“…For algorithm implementation, we employed the scikit-learn library and adopted the one-vs-rest (OvR) training scheme [29]. To handle class imbalances, we assigned weights inversely proportional to the frequencies of each class in the input data.…”
Section: E Logistic Regressionmentioning
confidence: 99%
“…For algorithm implementation, we employed the scikit-learn library and adopted the one-vs-rest (OvR) training scheme [29]. To handle class imbalances, we assigned weights inversely proportional to the frequencies of each class in the input data.…”
Section: E Logistic Regressionmentioning
confidence: 99%
“…"Multinomial" mode was generally considered to be more accurate in multiple classification cases, but our results demonstrated that when all the hyper-parameters were set to the same except the pattern ("ovr" or "multinomial"), this conclusion was not tenable. "Ovr" classification method regards the classification task as multiple binary logistic regressions, while "multinomial" mode is to choose two classes from all data to estimate, and then, to choose another two classes from the rest data to judge till the end [29]. However, in the actual processing, only by calculating of the specific data could demonstrate which parameter configuration is suitable for this situation most.…”
Section: Classification Of Mirnas Based On Lr Modelmentioning
confidence: 99%