A Hybrid Method Using Lexicon-Based Approach and Naive Bayes Classifier for Arabic Opinion Question Answering

Khalifa, Khalid Al; Omar, Nazlia

doi:10.3844/jcssp.2014.1961.1968

Cited by 24 publications

(23 citation statements)

References 12 publications

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“…The unsupervised approach is based on sentiment lexicon (which is often built automatically by exploiting English dictionaries such as Wordnet or Sentimentwordnet,etc) [8,6]. The hybrid approach is a combination of these two [14,19]. However, most of the existing related research works have concentrated more on dialects such as Egyptian, Levantine , Jordanian etc.…”

Section: Introductionmentioning

confidence: 99%

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

Alqarafi

Adeel

Hawalah

et al. 2018

Lecture Notes in Computer Science

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In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%.

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Section: Introductionmentioning

confidence: 99%

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

Alqarafi

Adeel

Hawalah

et al. 2018

Lecture Notes in Computer Science

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“…Results showed an 84% polarity classification accuracy and an 81% subjectivity classification accuracy. The authors of [32] proposed a hybrid method that combines a lexicon-based approach with a NB classifier. Experimental results showed an accuracy of 91%.…”

Section: B Corpus-based Approachesmentioning

confidence: 99%

Sentiment classification techniques for Arabic language: A survey

Biltawi

Etaiwi

Tedmori

et al. 2016

2016 7th International Conference on Information and Communication Systems (ICICS)

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With the advent of online data, sentiment analysis has received growing attention in recent years. Sentiment analysis aims to determine the overall sentiment orientation of a speaker or writer towards a specific entity or towards a specific feature of a specific entity. A fundamental task of sentiment analysis is sentiment classification, which aims to automatically classify opinionated text as being positive, negative, or neutral. Although the literature on sentiment classification is quite extensive, only a few endeavors to classify opinionated text written in the Arabic language can be found. This paper provides a comprehensive survey of existing lexicon, machine learning, and hybrid sentiment classification techniques for Arabic language.

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“…Due to the challenge of interpreting the text [16], data visualization is used to aid in representing the information retrieved so that it can be more understandable [17]. In order to retrieve the polarity of individual words, lexicon-based sentiment analysis approach is used to categorize positive and negative sentiments using word matching [18][19]. From the lexicon-based approach, dictionary-based approach is applied instead of corpus-based approach.…”

Section: Preliminary Studymentioning

confidence: 99%

Visualization of Crime News Sentiment in Facebook

Haron¹,

Abidin²,

Zamani³

et al. 2018

IJET

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Facebook has become a popular platform in communicating information. People can express their opinions using texts, symbols, pictures and emoticons via Facebook posts and comments. These expressions allow sentiment analysis to be performed by collecting the data to obtain the public's opinions and emotions toward certain issues. Due to a huge amount of data obtained from Facebook, proper approaches are required to cater the texts and symbols used in the comments. There are also limited amount of dictionary on Malay texts which make it more challenging to process and classify the positive and negative words used in the comments. Thus, hybrid approach is applied during the data processing to visualize the results. In this work, a combination of lexicon-based approach and Naïve Bayes are used. This study focuses on analyzing the public's sentiments on crime news in Facebook by using word cloud visualization. The visualization displays important words used in a form of a word cloud. Moreover, the percentage of positive and negative words existed in the comments is also shown as part of the visualization results.

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A Hybrid Method Using Lexicon-Based Approach and Naive Bayes Classifier for Arabic Opinion Question Answering

Cited by 24 publications

References 12 publications

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

Sentiment classification techniques for Arabic language: A survey

Visualization of Crime News Sentiment in Facebook

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