A genetic algorithm feature selection based approach for Arabic Sentiment Classification

Aliane, Ahmed Amine; Aliane, Hassina; Ziane, Mikal; Bensaou, Nacéra

doi:10.1109/aiccsa.2016.7945661

Cited by 16 publications

(8 citation statements)

References 11 publications

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“…In ASA, N-grams models have been largely used as features. Some studies exposed that unigrams resulted in a better performance than bigrams and trigrams [35,54]. is behavior was due to the fact that BOW can give a good data coverage, whereas bigrams and trigrams tend to be very sparse.…”

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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“…Researchers have presented several methods and models to tackle Arabic SA based on three major approaches, namely, machine learning (ML) [36,37], semantic orientation [38,39], and hybrid approach [40]. In this research, we focus on the studies that employ the ML approach, specifically deep learning methods, which have been increasingly utilized in the past decade.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach Combining CNN and Bi-LSTM with SVM Classifier for Arabic Sentiment Analysis

Al-Harbi¹

2021

IJACSA

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Deep learning models have recently been proven to be successful in various natural language processing tasks, including sentiment analysis. Conventionally, a deep learning model's architecture includes a feature extraction layer followed by a fully connected layer used to train the model parameters and classification task. In this paper, we employ a deep learning model with modified architecture that combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, with Support Vector Machine (SVM) for Arabic sentiment classification. In particular, we use a linear SVM classifier that utilizes the embedded vectors obtained from CNN and Bi-LSTM for polarity classification of Arabic reviews. The proposed method was tested on three publicly available datasets. The results show that the method achieved superior performance than the two baseline algorithms of CNN and SVM in all datasets.

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“…In contrast, researchers in the eld of Arabic language SA are somewhat hampered by a relative shortage of resources, even though there are about 219 million Arabic internet users. Moreover, the application of SA to Arabic language text is relatively more complicated for a number of reasons, including the diversity of dialects that are used in each Arabic-speaking country or even region or city, as well as the limitations of existing resources and tools such as stemmers that are more suitable for use with Modern Standard Arabic (MSA), whereas most Arabic internet users prefer to give their opinions in microblogging and social media websites in their own dialects (Aliane, Aliane, Ziane, & Bensaou, 2016). This research aims to suggest new methodologies help in solving text mining and SA problems especially on the Arabic language, therefore, new approaches are presented in order to get more accurate results within a reasonable time and to make these approaches applicable to any dataset regardless to its domain.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Arabic Reviews Using a Hybrid Clustering Technique

Alweshah

Alahmed

Alkhalaileh

et al. 2022

Preprint

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Sentiment analysis (SA) is the process of assessing the sentiment and attitude of digital audiences toward a range of topics and subjects. The aim of this research is to propose an effective approach for finding good-quality solutions for dialectal Arabic SA problems by addressing inherent challenges in an optimal way. This is achieved by determining the polarities of review texts by using the k-means clustering algorithm in a lexicon-based model and also applying a ML model where necessary in a hybrid approach. In this research, a sentiment lexicon (senti-lexicon) corpus of 3,824 positive and negative words/terms is used in a deep feature extraction process to convert the text into feature vectors. The experimental results showed that the k-means clustering model worked better after separating the observations with relative score values and moving them to be classified using the lexicon-based model. The k-means clustering model part of the hybrid model yielded high-performance results in terms of accuracy, recall, and F1 score metrics, especially in the positive and negative score value features and total score. Each technique has shortcomings, the hybrid model; as the results that are shared will represent; prove that it is an ideal and more flexible solution and approach to conducting SA in an effective and self-improving manner.

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A genetic algorithm feature selection based approach for Arabic Sentiment Classification

Cited by 16 publications

References 11 publications

Arabic Sentiment Analysis: A Systematic Literature Review

Arabic Sentiment Analysis: A Systematic Literature Review

A Deep Learning Approach Combining CNN and Bi-LSTM with SVM Classifier for Arabic Sentiment Analysis

Sentiment Analysis of Arabic Reviews Using a Hybrid Clustering Technique

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