Aspect-based Sentiment Analysis for Arabic Content in Social Media

Alshammari, Norah Fahad; Almansour, Amal

doi:10.1109/icecce49384.2020.9179327

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Additionally, in Ref. [11], customers' sentiments were extracted-using machine learning and deep learning approaches-from 1098 tweets, collected by the authors, regarding the Saudi telecommunication companies STC, Mobile, and Zain. The paper was part of an ongoing project.…”

Section: Related Workmentioning

confidence: 99%

AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis

Almasaud

Al-Baity

2023

Sustainability

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The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. AraMA comprises 10,750 Google Maps reviews for restaurants in Riyadh, Saudi Arabia. It covers four aspect categories—food, environment, service, and price—along with four sentiment polarities: positive, negative, neutral, and conflict. All AraMA reviews are labeled with at least two aspect categories. A second version, named AraMAMS, includes reviews labeled with at least two different sentiments, making it the first Arabic multi-aspect, multi-sentiment dataset. AraMAMS has 5312 reviews covering the same four aspect categories and sentiment polarities. Both corpora were evaluated using naïve biased (NB), support vector classification (SVC), linear SVC, and stochastic gradient descent (SGD) models. In the AraMA corpus, the aspect categories task achieved a 91.41% F1 measure result using the SVC model, while in the AraMAMS corpus, the best F1 measure result for aspect categories task reached 91.70% using the linear SVC model.

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Section: Related Workmentioning

confidence: 99%

AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis

Almasaud

Al-Baity

2023

Sustainability

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“…As we only focus on the Twitter platform, we found various techniques have been used to collect Arabic tweets. Most studies used Twitter's official Application Programming Interface (API) to collect Arabic tweets [2,[33][34][35][36][37][38][39]. Other studies used trending hashtags to extract data manually [8,40].…”

Section: B Tools Used To Collect Arabic Tweetsmentioning

confidence: 99%

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Alqurashi

2023

Eng. Technol. Appl. Sci. Res.

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Social media platforms have a huge impact on our daily lives. They have succeeded in attracting many people to spend time communicating and expressing themselves. Twitter is a social media platform that could be considered as a source of public opinion about products, services, and events. Sentiment analysis is the art of studying public feelings about certain topics, which may be positive, negative, or neutral. This paper provides a systematic review of Arabic tweet sentiment analysis on papers published from 2012 to 2021 in digital libraries including IEEE Explorer, Science Direct, Springer Link, and Google Scholar. The main aim of this systematic review is to investigate the trends in the topics reported and to highlight potential new research lines. To achieve that, three main stages were implemented: planning, conducting, and reporting the review. Our findings suggest the need for an open-source large Arabic tweet dataset that can be used by researchers. Also, it was found that researchers have used various classification techniques, which led to different results.

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“…The aspect extraction task is called aspect detection, whereas sentiment classification is called aspect-polarity detection. Aspect-based sentiment analysis has been implemented in various foreign languages, such as English [ 34 ], Arabic [ 35 ], and Burmese [ 36 ].…”

Section: Related Workmentioning

confidence: 99%