Sentimentanalysis for Arabic and English Datasets

El-Awady, Rasheed M.; Barakat, Sherif; El-Rashidy, Nora Mahmud

doi:10.21608/ijicis.2015.10911

Cited by 6 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From these Al-Osaimi et al [81], gained a valuable accuracy from other technique. Harrag et al [86], Motaz et al [87], Rasheed et al [88] used the decision tree model and got handsome results on the various corpus of the Arabic language. Another study was proposed by Hammad et al [89] that used the data obtained from 2000 Arabic reviews from social media for evaluation.…”

Section: Discussion and Learned Lessonsmentioning

confidence: 99%

Classification of Arabic Tweets: A Review

Alruily

2021

Electronics

View full text Add to dashboard Cite

Text classification is a prominent research area, gaining more interest in academia, industry and social media. Arabic is one of the world’s most famous languages and it had a significant role in science, mathematics and philosophy in Europe in the middle ages. During the Arab Spring, social media, that is, Facebook, Twitter and Instagram, played an essential role in establishing, running, and spreading these movements. Arabic Sentiment Analysis (ASA) and Arabic Text Classification (ATC) for these social media tools are hot topics, aiming to obtain valuable Arabic text insights. Although some surveys are available on this topic, the studies and research on Arabic Tweets need to be classified on the basis of machine learning algorithms. Machine learning algorithms and lexicon-based classifications are considered essential tools for text processing. In this paper, a comparison of previous surveys is presented, elaborating the need for a comprehensive study on Arabic Tweets. Research studies are classified according to machine learning algorithms, supervised learning, unsupervised learning, hybrid, and lexicon-based classifications, and their advantages/disadvantages are discussed comprehensively. We pose different challenges and future research directions.

show abstract

Section: Discussion and Learned Lessonsmentioning

confidence: 99%

Classification of Arabic Tweets: A Review

Alruily

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…13 out of the 24 papers reviewed used Twitter as a source of data. Other studies used different data sources, including Aljazeera (Duwairi & El-Orfali, 2014), Facebook and Blogs (Akaichi et al, 2013), Amazon Reviews (Jain et al, 2016), goodreads.com (Al Shboul et al, 2015, Aljazeera movie reviews (Bayoudhi et al, 2015), YouTube comments (Elawady et al, 2015;Baccouche et al, 2019), Multi-domain reviews (ElSahar & El-Beltagy, 2015), hotel reviews (Cherif et al, 2015), book reviews (Al Shboul et al, 2015), subjectivity dataset (Hammad & Mouhammd, 2016;Poecze et al, 2018), and a combination of Hotels reviews, Facebook, Twitter, and YouTube (Hammad & Mouhammd, 2016).…”

Section: Datasets Usedmentioning

confidence: 99%

Untitled

2022

OJIT

View full text Add to dashboard Cite

The purpose of this paper is to review various studies on current machine learning techniques used in sentiment analysis with the primary focus on finding the most suitable combinations of the techniques, datasets, data features, and algorithm performance parameters used in most applications. To accomplish this, we performed a systematic review of 24 articles published between 2013 and 2020 covering machine learning techniques for sentiment analysis. The review shows that Support Vector Machine as well as Naïve Bayes techniques are the most popular machine learning techniques; word stem and n-grams are the most extensively applied features, and the Twitter dataset is the most predominant. This review further revealed that machine learning algorithms' performance depends on many factors, including the dataset, extracted features, and size of data used. Accuracy is the most commonly used algorithm performance metric. These findings offer important information for researchers and businesses to use when selecting suitable techniques, features, and datasets for sentiment analysis for various business applications such as brand reputation monitoring.

show abstract

“…These features were used to train several machinelearning algorithms for classification, mainly SVM, Multinomial Naïve Bayes (MNB), Conditional Random Fields (CRF), Decision Trees, and k-Nearest Neighbors (k-NN). Overall, in some cases, SVM achieved better results [21,48,57,58,66,90,91,100,101,110,144,145,147,148,175,194,195,205,206,218,237,307,351,363,365], and in other cases, NB performed better [51,115,117,191,257,286], especially in the case of unbalanced datasets such as in References [306,308,309]. Mostafa [304] claimed that the best classifier is dataset dependent.…”

Section: Feature Engineering "Supervised" Approachesmentioning

confidence: 99%

A Survey of Opinion Mining in Arabic

Badaro

Baly

Hajj

et al. 2019

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

show abstract

Sentimentanalysis for Arabic and English Datasets

Cited by 6 publications

References 5 publications

Classification of Arabic Tweets: A Review

Classification of Arabic Tweets: A Review

Untitled

A Survey of Opinion Mining in Arabic

Contact Info

Product

Resources

About