BERT-Based Joint Model for Aspect Term Extraction and Aspect Polarity Detection in Arabic Text

Chouikhi, Hasna; Alsuhaibani, Mohammed; Jarray, Fethi

doi:10.3390/electronics12030515

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…Furthermore, [42] tested a transfer-learning approach using Arabic-BERT-CRF for tasks T1 and T2 on a human-annotated Arabic dataset for ABSA. The experimental results demonstrated that the model exceeded the baseline model, which relied on conditional random fields (CRF) with features extracted using named entity recognition (NER), POS tagging, parsing, semantic analysis, and other recently proposed models such as AraBERT, MarBERT, and CamelBERT-MSA.…”

Section: ) Deep Learning 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: ) Deep Learning 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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“…Another deep learning model for manipulating Arabic sentiments was presented in [49], where the data was collected based on three classes and then classified using the LSTM model to explore the results. One of the recent methods for extracting and detecting Arabic polarity text was proposed in [50], where transfer learning (TL) techniques were applied to aspect-based Arabic text. The authors proposed an architecture using the BERT model on the HAAD dataset to measure the approach's effectiveness, which showed high results.…”

Section: Machine Learning-based Approachmentioning

confidence: 99%

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

et al. 2023

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Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.

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“…It is an important topic of interest and an active field of study in all languages, especially for languages that lack sufficient scientific research and resources, such as datasets, lexicons, corpora, and tools [2]. One of these languages is the Arabic language, where the progress in Arabic opinion mining does not fit with the substantial Arab world population, contributing to generating enormous amounts of Arabic data on the web [3][4][5]. The other challenges of the Arabic language, including being inherently complex and using dialects of Arabic, are also slowing research advancement [6].…”

Section: Introductionmentioning

confidence: 99%

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Al-Mutawa

Al-Aama

2023

Applied Sciences

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Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.

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BERT-Based Joint Model for Aspect Term Extraction and Aspect Polarity Detection in Arabic Text

Cited by 16 publications

References 18 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

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

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