Aspect-Based Sentiment Analysis of Arabic Tweets in the Education Sector Using a Hybrid Feature Selection Method

Alassaf, Manar; Qamar, Ali Mustafa

doi:10.1109/iit50501.2020.9299026

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…They used recent online learning graduates feedback from BINUS (Bina Nusantara University). Moreover, [28] proposed a hybrid features selection method to address T1 and T2 in Arabic tweets related to Qassim University. It extracts aspects related to the education domain such as teaching quality, services, activities, etc.…”

Section: A Absa In Educational Domainmentioning

confidence: 99%

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Alshaikh,

Almatrafi,

Abushark

2024

IEEE Access

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Educational institutions typically gather feedback from beneficiaries through formal surveys.Offering open-ended questions allows students to express their opinions about matters that may not have been measured directly in closed-ended questions. However, responses to open-ended questions are typically overlooked due to the time and effort required. Aspect-based sentiment analysis is used to automate the process of extracting fine-grained information from texts. This study aims to 1) examine the performance of different BERT-based models for aspect term extraction for Arabic text sourced from educational institution surveys; 2) develop a system that automates the ABSA process in a way that will automatically label survey responses. An end-to-end system was developed as a case study to extract aspect terms, identify their polarity, map extracted aspects to their respective categories, and aggregate category polarity. To accomplish this, the models were evaluated using an in-house dataset. The result showed that FAST-LCF-ATEPC, a multilingual checkpoint, outperformed other models including AraBERT, MARBERT, and QARiB, in the aspect-term extraction task, with an F1 score of 0.58. Hence, it was used for aspect-term polarity classification, showing an F1 score of 0.86. Mapping aspects to their respective categories using a predefined list yielded an average F1 score of 0.98. Furthermore, the polarities of the categories were aggregated to summarize the overall polarity for each category. The developed system can support Arabic educational institutions in harnessing valuable information in responses to open-ended survey questions, allowing decision-makers to better allocate resources, and improve facilities, services, and students' learning experiences.

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Section: A Absa In Educational Domainmentioning

confidence: 99%

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Alshaikh,

Almatrafi,

Abushark

2024

IEEE Access

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“…In 2020, a total of 7934 tweets related to Qassim university were collected by the author of [12]. Annotators labelled aspect categories and sentiment polarities.…”

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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“…ABSA can help identify investor sentiment towards dif-ferent aspects of a company or its financial performance, potentially predicting stock market trends (Sinha et al, 2022;de França Costa and da Silva, 2018;Ong et al, 2023;Hridoy et al, 2021). Education datasets could uncover student sentiments towards specific aspects of the learning environment, leading to targeted improvements (Alassaf and Qamar, 2020).…”

Section: Is Absa Only For Reviews?mentioning

confidence: 99%

A Review of Datasets for Aspect-based Sentiment Analysis

Chebolu,

Dernoncourt,

Lipka

et al. 2023

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacifi

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Aspect-based sentiment analysis (ABSA) is a natural language processing problem that analyzes user-generated reviews to determine a) the target entity being reviewed, b) the highlevel aspect to which it belongs, and c) the sentiment expressed toward the targets and the aspects. Numerous yet scattered corpora for ABSA make it difficult for researchers to identify corpora best suited for a specific ABSA subtask quickly. This study presents a database of corpora that can be used to train and evaluate autonomous ABSA systems. Additionally, we provide an overview of the major corpora for ABSA and its subtasks and highlight several features that researchers should consider when selecting a corpus. Finally, we discuss the advantages and disadvantages of existing dataset collection approaches and make recommendations for future corpora creation. This survey examines 98 publicly available ABSA datasets covering over 25 domains, including 77 English and 21 other languages datasets (https://github.com/RiTUAL-UH/ ABSA-Datasets-Info).

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Aspect-Based Sentiment Analysis of Arabic Tweets in the Education Sector Using a Hybrid Feature Selection Method

Cited by 12 publications

References 17 publications

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

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

A Review of Datasets for Aspect-based Sentiment Analysis

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