Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews

Jararweh, Yaser; Al‐Ayyoub, Mahmoud; Al-Smadi, Mohammad; Obaidat, Islam; Mohawesh, Rami

doi:10.4018/ijitwe.2016070102

Cited by 19 publications

(3 citation statements)

References 46 publications

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“…A comparative study was conducted to test and assess various lexicon-based approaches for ABSA tasks T3 and T4 based on 63,000 book reviews annotated by humans [29]. This was later extended using enhanced lexicon-based approaches on the same book review dataset to achieve results that exceeded those of the previous study, particularly for T4 (accuracy:0.88) and T3 (F1 score:0.24) [30]. Several studies have combined two approaches or models to produce superior models.…”

Section: ) Unsupervised Approachesmentioning

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: ) Unsupervised Approachesmentioning

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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“…Lexicon-based approaches use an opinion lexicon [18]. Al-Smadi et al [19] has developed several lexicon-based approaches to address two ABSA sub-tasks (T3 and T4). They report that they are the pioneers of ABSA in Arabic, as they developed an Arabic Human Annotated Data Set (HAAD), consisting of reviews of Arabic books, and made it publicly available [10].…”

Section: Related Workmentioning

confidence: 99%

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Behdenna¹,

Belalem²,

Barigou³

2022

IJCDS

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Currently, one of the most important and difficult areas of research is Arabic Sentiment analysis. Aspect extraction is the most important task in aspect-based sentiment analysis. In this work, we propose an aspect-based sentiment analysis approach for Arabic language. The basic idea of the proposed approach is to use the description logics to identify explicit aspects as well as their polarities. This approach was evaluated using the publicly available human annotated Arabic dataset (HAAD) based on Semantic Evaluation 2014 workshop (SemEval-2014) along with its baseline experiments conducted by HAAD providers. Experimental results showed that the proposed approach significantly improves the performance compared with baseline approach. The F-measure was improved by 19% and 55% for the aspect terms extraction and aspect term polarity tasks respectively.

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“…They started by (1) building a high-coverage sentiment lexicon with a semiautomatic approach; (2) creating a large multi-domain annotated sentiment corpus segmented into discourse segments to evaluate the sentiment approach; and (3) applying a lexicon-based approach with an aggregation model taking into account challenging linguistic phenomena such as negation and intensification. Obaidata et al [69,320] proposed a lexicon-based approach for aspect extraction and classification of sentiments toward those aspects. They experimented on a dataset HAAD [71].…”

Section: Lexicon-based Approachesmentioning

confidence: 99%

A Survey of Opinion Mining in Arabic

Badaro

Baly

Hajj

et al. 2019

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

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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.

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Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews

Cited by 19 publications

References 46 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

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

A Survey of Opinion Mining in Arabic

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