Mining Text Data

Aggarwal, Charų C.

doi:10.1007/978-3-319-14142-8_13

Cited by 176 publications

(103 citation statements)

References 257 publications

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“…is the number of tweets class occurring without . is the number of tweets of other classes without (Aggarwal et al, 2012).…”

Section: Chi-squarementioning

confidence: 99%

Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting

Altaher

2017

Int. j. adv. appl. sci.

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The increasing adoption of social media networks as a platform for sharing opinions on different aspects emerged the sentiment analysis and opinion mining as an active research area. Recently, the sentiment analysis on Twitter has attracted considerable attention due to its many applications in various aspects of our lives. Many approaches have been presented for sentiment analysis based on English language, thus there is a need for efficient sentiment analysis approaches for Arabic language, since it has different structure when compared to other languages. This paper proposes a hybrid approach for sentiment analysis of Arabic tweets based on two stages. Firstly, the pre-processing methods like stop-word removal, tokenization and stemming are applied, and then two features weighting algorithms (information gain and chai square) are utilized to assign high weights to the most significant features of the Arabic tweets. Secondly, the deep learning technique is employed to effectively and accurately classify the Arabic tweets either as positive or negative tweets. The performance of the proposed approach has been compared with some of the classification methods such as Decision Tree (DT), Neural Networks (NN) and Support Vector Machine (SVM) using the dataset collected from Arabic tweets. The proposed approach outperforms the other approaches and achieved highest accuracy and precision of 90% and 93.7%, respectively.

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“…is the number of tweets class occurring without . is the number of tweets of other classes without (Aggarwal et al, 2012).…”

Section: Chi-squarementioning

confidence: 99%

Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting

Altaher

2017

Int. j. adv. appl. sci.

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“…Esse classificadoré uma técnica probabilística baseada no Teorema de Bayes [Aggarwal and Zhai 2012]. Ele calcula a probabilidade que uma amostra desconhecida pertença a cada uma das classes possíveis, predizendo a classe mais provavél.…”

Section: Redes Bayseanasunclassified

“…Rede neural artificalé uma técnica de aprendizagem de máquina que simula o funcionamento de um sistema nervoso [Aggarwal and Zhai 2012]. Para isso, conta com a presença de neurônios artificiais interligados entre si por meio de sinapses (na computação pesos).…”

Section: Rbfunclassified

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Uma abordagem computacional de análise de opinião para identificação de preconceito em redações

Neto

Cavalcanti

Costa³

et al. 2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

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Abstract. The large adoption of the ENEM grade as an instrument to evaluate students in higher education, the number of people performing this test increased significantly. Among the evaluation methods, this test includes an essay. In this exam is required to respect human rights. In this context, this work proposes the analysis of different algorithms for the identification of hate speech in essays. It adopts a hybrid approach combining machine learning techniques and lexical dictionaries. Several experiments were conducted and in the best case the proposal reached a f-measure of 82.10%. Finally, a qualitative study was carried out where essays evaluators indicated that the adoption of the proposed tool would aid in the correction of essays.Resumo. Com a grande adoção da nota ENEM como instrumento para avaliar estudantes no ingresso do ensino superior, o número de pessoas que passou a fazer essa prova aumentou significativamente. Dentre as etapas da prova existe uma redação que avalia, entre outras coisas, se o estudante fez o seu texto respeitando os direitos humanos. Diante disto, este trabalho propõe a análise de diferentes algoritmos para identificação de frases preconceituosas em redações. Para isso, foi utilizada uma abordagem híbrida combinando técnicas de aprendizagem de máquina e dicionários léxicos. Foram realizados diversos experimentos, no melhor caso a proposta atingiu uma f-measure de 82,10%. Por fim, foi realizado um estudo qualitativo onde avaliadores de redação indicaram que a adoção da ferramenta proposta auxiliaria na correção de redações.

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“…NED determines which concrete entity a particular word or phrase refers to. The combination of both techniques have been shown [5] to be a powerful mechanism to turn natural language text into a structured representation that machines can reason about.…”

Section: A Concept Extraction Overviewmentioning

confidence: 99%

Matching GitHub Developer Profiles to Job Advertisements

Hauff

Gousios

2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories

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Abstract-GitHub is a social coding platform that enables developers to efficiently work on projects, connect with other developers, collaborate and generally "be seen" by the community. This visibility also extends to prospective employers and HR personnel who may use GitHub to learn more about a developer's skills and interests. We propose a pipeline that automatizes this process and automatically suggests matching job advertisements to developers, based on signals extracting from their activities on GitHub.

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Mining Text Data

Cited by 176 publications

References 257 publications

Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting

Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting

Uma abordagem computacional de análise de opinião para identificação de preconceito em redações

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