A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

Duwairi, Rehab; El-Orfali, Mahmoud

doi:10.1177/0165551514534143

Cited by 147 publications

(96 citation statements)

References 16 publications

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“…Duwairi and El-Orfali [26] performed a study on SA for Arabic text. e authors used two datasets: one prepared inhouse related to politics domain and the other prepared by Rushdi-Saleh et al [8] related to movie domain.…”

Section: Discussion and Future Research Avenuesmentioning

confidence: 99%

“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

confidence: 99%

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Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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Section: Discussion and Future Research Avenuesmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…The tweets and reviews used for sentiment analysis often contain noise in the form of words that do not contribute towards the classification and hence pre-processing is an important task in sentiment analysis. For example, research [19] employs sentiment analysis on Arabic tweets. The impact of stemming, feature correlation, and n-grams model is thoroughly investigated in Arabic text.…”

Section: Research Work On the Pre-processing In Sentiment Analysismentioning

confidence: 99%

GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier

et al. 2020

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User reviews on social networking platforms like Twitter, Facebook, and Google+, etc. have been gaining growing interest on account of their wide usage in sentiment analysis which serves as the feedback to both public and private companies, as well as, governments. The analysis of such reviews not only plays a noteworthy role to improve the quality of such services and products but helps to devise marketing and financial strategies to increase the profit for companies and customer satisfaction. Although many analysis models have been proposed, yet, there is still room for improving the processing, classification, and analysis of user reviews which can assist managers to interpret customers feedback and elevate the quality of products. This study first evaluates the performance of a few machine learning models which are among the most widely used models and then presents a voting classifier Gradient Boosted Support Vector Machine (GBSVM) which is constituted of gradient boosting and support vector machines. The proposed model has been evaluated on two different datasets with term frequency and three variants of term frequency-inverse document frequency including uni-, bi-, and tri-gram as features. The performance is compared with other state-of-the-art techniques which prove that GBSVM outperforms these models.

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“…The recent studies (Aldayel & Azmi, 2016;Asghar, Ahmad, Qasim, Zahra, & Kundi, 2016;Duwairi & El-Orfali, 2014;Ikeda, Takamura, Ratinov, & Okumura, 2008) have demonstrated that their sentiment update methods produce promising results with respect to accurate scoring of domainspecific words in the sentiment analysis of user reviews. They achieved significant improvement in accuracy in both the hotel and movie domains.…”

Section: Updating a Term's Sentiment Scorementioning

confidence: 99%

T‐SAF: Twitter sentiment analysis framework using a hybrid classification scheme

et al. 2017

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Of the many social media sites available, users prefer microblogging services such as Twitter to learn about product services, social events, and political trends. Twitter is considered an important source of information in sentiment analysis applications. Supervised and unsupervised machine learning‐based techniques for Twitter data analysis have been investigated in the last few years, often resulting in an incorrect classification of sentiments. In this paper, we focus on these issues and present a unified framework for classifying tweets using a hybrid classification scheme. The proposed method aims at improving the performance of Twitter‐based sentiment analysis systems by incorporating 4 classifiers: (a) a slang classifier, (b) an emoticon classifier, (c) the SentiWordNet classifier, and (d) an improved domain‐specific classifier. After applying the preprocessing steps, the input text is passed through the emoticon and slang classifiers. In the next stage, SentiWordNet‐based and domain‐specific classifiers are applied to classify the text more accurately. Finally, sentiment classification is performed at sentence and document levels. The findings revealed that the proposed method overcomes the limitations of previous methods by considering slang, emoticons, and domain‐specific terms.

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A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

Cited by 147 publications

References 16 publications

Arabic Sentiment Analysis: A Systematic Literature Review

Arabic Sentiment Analysis: A Systematic Literature Review

GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier

T‐SAF: Twitter sentiment analysis framework using a hybrid classification scheme

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