INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic
            Programming for Twitter Sentiment Analysis

Miranda-Jiménez, Sabino; Graff, Mario; Téllez, Eric Sadit; Moctezuma, Daniela

doi:10.18653/v1/s17-2130

Cited by 11 publications

(5 citation statements)

References 10 publications

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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%

Improving classification performance by feature space transformations and model selection

Ortiz-Bejar¹,

Téllez²,

Graff³

2019

Preprint

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Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, our objective is to combine them to transform data's shape into another more convenient distribution; such that some simple algorithms, such as Naïve Bayes or k-Nearest Neighbors, can produce competitive classifiers. In this paper, we introduce a family of classifiers based on feature mapping and kernel functions, orchestrated by a model selection scheme that excels in performance.We provide an extensive experimental comparison of our methods with sixteen popular classifiers on more than thirty benchmarks supporting our claims. In addition to their competitive performance, our statistical tests also found that our methods are different among them, supporting our claim of a compelling family of classifiers.

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Section: Resultsmentioning

confidence: 99%

Improving classification performance by feature space transformations and model selection

Ortiz-Bejar¹,

Téllez²,

Graff³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

Improving k Nearest Neighbors and Naïve Bayes Classifiers Through Space Transformations and Model Selection

et al. 2020

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“…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%

A Survey of Opinion Mining in Arabic

Badaro

Baly

Hajj

et al. 2019

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

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INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis

Cited by 11 publications

References 10 publications

Improving classification performance by feature space transformations and model selection

Improving classification performance by feature space transformations and model selection

Improving k Nearest Neighbors and Naïve Bayes Classifiers Through Space Transformations and Model Selection

A Survey of Opinion Mining in Arabic

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