A  Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis

AL-Jumaili, Ahmed Sabah Ahmed

doi:10.21123/bsj.2020.17.1(suppl.).0385

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…Twitter has received attention from researchers, as in [16,17]. Some of the research on ASA is dedicated to analysing opinions in social media (Facebook, Twitter, YouTube), as in [17][18][19][20][21]. Many available datasets for testing proposed methods of ASA for standard Arabic can be found in [19,20,22].…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

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Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users' reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users' reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users' opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.

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Section: Literature Reviewmentioning

confidence: 99%

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The results showed that k-means clustering achieved the best accuracy. The author in [39] suggested combining linguistic and statistical features and sentiment classification using a tweets dataset in Arabic. They used three classifiers: SVM, KNN and ME.…”

Section: Reviews Arabic Language Sentiment Classificationmentioning

confidence: 99%

Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

Alharbi,

Mezher,

Barakeh

2022

IJACSA

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Now-a-days, social media sites and travel blogs have become one of the most vital expression sources. Tourists express everything related to their experiences, reviews, and opinions about the place they visited. Moreover, the sentiment classification of tourist reviews on social media sites plays an increasingly important role in tourism growth and development. Accordingly, these reviews are valuable for both new tourists and officials to understand their needs and improve their services based on the assessment of tourists. The tourism industry anywhere also relies heavily on the opinions of former tourists. However, most tourists write their reviews in their local dialect, making sentiment classification more difficult because there are no specific rules to control the writing system. Moreover, there is a gap between Modern Standard Arabic (MSA) and local dialects. one of the most prominent issues in sentiment analysis is that the local dialect lexicon has not seen significant development. Although a few lexicons are available to the public, they are sparse and small. Thus, this paper aims to build a model capable of accurate sentiment classification in the Saudi dialect for Arabic in tourist place reviews using deep learning techniques. Machine learning techniques help classifying these reviews into (positive, negative, and neutral). In this paper, three machine learning algorithms were used, Support -Vector Machine (SVM), Long short-term memory (LSTM), and Recurrent Neural Network (RNN). These algorithms are classified using Google Map data set for tourist places in Saudi Arabia. Performance classification of these algorithms is done using various performance measures such as accuracy, precision, recall and Fscore. The results show that the SVM algorithm outperforms the deep learning techniques. The result of SVM was 98%, outperforming the LSTM, and RNN had the same performance of 96%.

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“…a) Fifth phase: Hybrid supervised classification approach phase: This phase performs two subsections: the first issue is applying ML approach which performs five selected ML classifiers which utilized extensively for ASA: Logistic Regression (LR) [25] [26] [27] [28], Naïve Bayes (NB) [29] [30] [31] [32], K-Nearest Neighbors (KNN) [33] [34] [31] [35], Random Forest (RF) [36] [37] [38] and SVM [33] [39] [40] in addition applying DL approach which performs DL classifier Multi-Layer Perceptron Neural Network (MLP-NN) which applied in [36] [37] for ASA.…”

Section: ) Stop Word Removalmentioning

confidence: 99%

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Saudy¹,

El-Ghazaly²,

Nasr³

et al. 2022

IJACSA

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Arabic language incurs from the shortage of accessible huge datasets for Sentiment Analysis (SA), Machine Learning (ML), and Deep Learning (DL) applications. In this paper, we present MASR, a simple Mobile Applications Arabic Slang Reviews dataset for SA, ML, and DL applications which comprises of 2469 Egyptian Mobile Apps reviews, and help app developers meet user requirements evolution. Our methodology consists of six phases. We collect mobile apps reviews dataset, then apply preprocessing steps, in addition perform SA tasks. To evaluate MASR datasets, first we apply ML classification techniques: K-Nearest Neighbors (K-NN), Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF), and DL classification technique: Multi-layer Perceptron Neural Network (MLP-NN). From the examination for pervious classification techniques, we adopted a hybrid classification approach combined from the top two ML classifier accuracy results (LR, RF), and DL classifier (MLP-NN). The findings prove the adequacy of a hybrid supervised classification approach for MASR datasets.

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A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis

Cited by 11 publications

References 13 publications

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

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