Advanced Automatic Lexicon with Sentiment Analysis Algorithms for Arabic Reviews

Mostafa, Ayman Mohamed

doi:10.3844/ajassp.2017.754.765

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…The authors in this paper proposed a methodology for detecting negation terms even if they do not precede the polarity term. Another enhanced sentiment analysis mechanism for analyzing Arabic reviews was proposed in [25], where a lexicon-based analyzer was constructed to analyze polarity terms with different weights to increase efficiency.…”

Section: Lexicon-based Approachmentioning

confidence: 99%

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

et al. 2023

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Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.

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Section: Lexicon-based Approachmentioning

confidence: 99%

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

et al. 2023

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“…As proposed in our papers [41,42], automated sentiment analysis is applied for analyzing sentiments based on a predefined lexicon. The main objective is to create a lexicon that obtains Arabic linguistic terms from different domains and then analyze these sentiments to measure the accuracy.…”

Section: Automated Sentiment Analysis Modelsmentioning

confidence: 99%

“…The main objective is to create a lexicon that obtains Arabic linguistic terms from different domains and then analyze these sentiments to measure the accuracy. As presented in [41], a multi-weight lexicon is created for analyzing Arabic sentiments. The lexicon is created using different classical and slang Arabic terms and the data cleaning is applied for removing stop words and objective terms that do not have polarity.…”

Section: Automated Sentiment Analysis Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

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Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process. Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process. This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research. A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms. An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms. An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically. The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

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“…Later, four advanced rules were proposed to address the negation detection task in the Saudi dialect, which enhanced the performance of the ASA task by 3% in terms of classification accuracy [21]. In 2017, Mostafa proposed an algorithm to detect negation, which depends on a lexicon of the common negation terms in the MSA and DA forms [22]. It identifies the negation cues and then searches for the polarity terms in all the tokens after this negation cue.…”

Section: Related Workmentioning

confidence: 99%

Arabic Negation and Speculation Scope Detection: a Transformer-based Approach.

Mahany

Khaled

Ghoniemy

2023

IJICIS

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Detecting the negation and speculation linguistic phenomena is vital for the performance of Arabic Natural Language Processing (ANLP) tasks. The negation and speculation scope detection problems have been addressed in a number of studies where most of them focused on the English and Spanish languages. This is due to the lack of corpora annotated for negation and speculation. In this work, the ArNeg corpus, annotated with negation, is extended by annotating it for the speculation to build the ArNegSpec corpus. In addition, we propose a transformer-based learning approach for detecting both the negation and speculation in Arabic texts. The AraBERT models with a Bidirectional Long Short-Term Memory and a Conditional Random Field (BiLSTM-CRF) as a sequence classification layer to achieve this goal. The results reached an F1 measure of 98% for cue identification for both negation and speculation. The proposed approach enhanced the evaluation results of the negation scope detection by 6% in terms of the F1 measure compared to the previous study. Furthermore, it achieved a 95% F1 measure for the speculation scope detection and a PCS value of 96% for both the negation and speculation scope. This approach shows the feasibility of transformerbased learning models in the sequence classification tasks as the detection of the negation and speculation in Arabic.

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Advanced Automatic Lexicon with Sentiment Analysis Algorithms for Arabic Reviews

Cited by 5 publications

References 18 publications

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Arabic Negation and Speculation Scope Detection: a Transformer-based Approach.

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