Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) 2017
DOI: 10.18653/v1/s17-2130
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INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis

Abstract: This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a nonlinear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our syste… Show more

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Cited by 11 publications
(5 citation statements)
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“…Most of these datasets were collected from UCI's Machine Learning Repository 3 with the exception of semeval and tassgeneralcorpus. These datasets were generated from the datasets provided for two Twitter Sentiment Analysis challenges, namely, TASS'16 (Spanish Sentiment Analysis, General Corpus [28]) and SemEval'2017 (Task 4: English Sentiment Analysis [30]). Text's feature vectors were computed using the fastText tool.…”
Section: Resultsmentioning
confidence: 99%
“…Most of these datasets were collected from UCI's Machine Learning Repository 3 with the exception of semeval and tassgeneralcorpus. These datasets were generated from the datasets provided for two Twitter Sentiment Analysis challenges, namely, TASS'16 (Spanish Sentiment Analysis, General Corpus [28]) and SemEval'2017 (Task 4: English Sentiment Analysis [30]). Text's feature vectors were computed using the fastText tool.…”
Section: Resultsmentioning
confidence: 99%
“…Most of these datasets were collected from UCI's Machine Learning Repository 4 with the exception of semeval and tass. These datasets were generated from the datasets provided for two Twitter Sentiment Analysis challenges, namely, TASS'16 (Spanish Sentiment Analysis, General Corpus [43]) and SemEval'2017 (Task 4: English Sentiment Analysis [45]). Text's feature vectors were computed using the fastText tool 5 .…”
Section: Random Searchmentioning
confidence: 99%
“…SiTAKA was ranked second in subtask A (sentiment classification). The INGEOTEC team used an ensemble classification system and ranked fourth in subtask A [294]. In this system, the output of a generic sentiment classification (B4MSA) system [378] was combined using the EvoDAG Genetic programming system [221,222].…”
Section: Feature Engineering "Supervised" Approachesmentioning
confidence: 99%