MARSA: Multi-Domain Arabic Resources for Sentiment Analysis

Alowisheq, Areeb; Al-Twairesh, Nora; Altuwaijri, Mawaheb I.; Almoammar, Afnan; Alsuwailem, Alhanouf Abdulrahman Saleh; Albuhairi, Tarfa; Alahaideb, Wejdan; Alhumoud, Sarah

doi:10.1109/access.2021.3120746

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…Two Arabic studies introduced large-scale public lexicons [33,34] to contribute more resources to Arabic opinion mining. Al-Twairesh et al [33] presented a vast lexicon of Arabic tweets annotated for sentiment analysis, known as the AraSenTi lexicon.…”

Section: Arabic Text-based Lexiconsmentioning

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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Section: Arabic Text-based Lexiconsmentioning

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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“…Multi-domain Arabic resources for sentiment analysis (MARSA): The largest annotated Gulf dataset was provided by the Arabic sentiment analysis research group at Imam Muhammad Ibn Saud Islamic University [42]. This includes several domains; however, the social and sport domains were used as two independent datasets in this study.…”

Section: Datasetsmentioning

confidence: 99%

TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis

Alshehri

Algarni

2023

Electronics

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In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.

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“…Our experiment consists in training SVM models on four different datasets: Arabic Sentiment Twitter Dataset (ASTD) [9], Moroccan Sentiment Twitter Dataset (MSTD) [10], Arabic Speech Act and Sentiment (ArSAS) [11], and Multi-Domain Arabic Resources for Sentiment Analysis (MARSA) [12]. Since each dataset was designed for a different purpose, we have only conserved Positive and Negative tweets in each dataset to assess SVM' s performance on 2-way sentiment classification applied to various datasets with several features.…”

Section: Experimental Studymentioning

confidence: 99%

Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

Banou¹,

Elfilali²,

Benlahmar³

2023

Math. Model. Comput.

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Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score. However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming. In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange polynomial.

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MARSA: Multi-Domain Arabic Resources for Sentiment Analysis

Cited by 13 publications

References 28 publications

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

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

TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis

Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

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