Negation-Aware Framework for Sentiment Analysis in Arabic Reviews

Duwairi, Rehab; Alshboul, Mohammad

doi:10.1109/ficloud.2015.115

Cited by 14 publications

(9 citation statements)

References 18 publications

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“…A few Arabic studies have addressed the impact of negation on SA – but only in the simple situation in which the polarity of only the sentiment terms following the negators are switched. Duwairi and Alshboul [46] presented a set of rules for handling negation for MSA. They used negation particles in MSA (mA-ما, lA-لا, ln-لن, lm-لم, lys-ليس).…”

Section: Results Enhancementmentioning

confidence: 99%

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Assiri

Emam

Al-Dossari

2017

Journal of Information Science

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Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.

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Section: Results Enhancementmentioning

confidence: 99%

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Assiri

Emam

Al-Dossari

2017

Journal of Information Science

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“…In the future, we aim to collect new data to study the sentiment regarding other COVID-19 related issues, such as COVID vaccines and their side effects. Moreover, we aim to investigate and apply more optimisation techniques to the pre-processing stage, such as applying some frameworks [ 25 ] to handle negation words for Arabic language, and to study their effect on the level of accuracy obtained. Furthermore, we may conduct comparative performance analysis after applying different sets of deep learning models, as well as testing of new transformer-based models for language representation, such as BERT [ 26 ], which was developed by Google, and has recently been pre-trained for the Arabic language [ 27 , 28 ] and proved to be efficient in different natural language processing domains, including sentiment analysis.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia during the COVID-19 Pandemic

Aljabri

Chrouf

Alzahrani

et al. 2021

Sensors

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The COVID-19 pandemic has greatly impacted the normal life of people worldwide. One of the most noticeable impacts is the enforcement of social distancing to reduce the spread of the virus. The Ministry of Education in Saudi Arabia implemented social distancing measures by enforcing distance learning at all educational stages. This measure brought about new experiences and challenges to students, parents, and teachers. This research measures the acceptance rate of this way of learning by analysing people’s tweets regarding distance learning in Saudi Arabia. All the tweets analysed were written in Arabic and collected within the boundary of Saudi Arabia. They date back to the day that the distance learning announcement was made. The tweets were pre-processed, and labelled positive, or negative. Machine learning classifiers with different features and extraction techniques were then built to analyse the sentiment. The accuracy results for the different models were then compared. The best accuracy achieved (0.899) resulted from the Logistic regression classifier with unigram and Term Frequency-Inverse Document Frequency as a feature extraction approach. This model was then applied on a new unlabelled dataset and classified to different educational stages; results demonstrated generally positive opinions regarding distance learning for general education stages (kindergarten, intermediate, and high schools), and negative opinions for the university stage. Further analysis was applied to identify the main topics related to the positive and negative sentiment. This result can be used by the Ministry of Education to further improve the distance learning educational system.

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“…The label of the input sentence is also flipped from positive to negative. In addressing the negation problem, we adopted the Negation-aware Framework presented by Duwairi & Alshboul (2015) , where the authors explore the effects of Arabic morphology on sentiment analysis. The study focused on five negations particles (لم، لن، لا، ما، ليس) that have been grouped into two categories based on their effect on the word as shown in Table 7 .…”

Section: Negationmentioning

confidence: 99%

Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis

Duwairi

Abushaqra

2021

PeerJ Computer Science

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Arabic language is a challenging language for automatic processing. This is due to several intrinsic reasons such as Arabic multi-dialects, ambiguous syntax, syntactical flexibility and diacritics. Machine learning and deep learning frameworks require big datasets for training to ensure accurate predictions. This leads to another challenge faced by researches using Arabic text; as Arabic textual datasets of high quality are still scarce. In this paper, an intelligent framework for expanding or augmenting Arabic sentences is presented. The sentences were initially labelled by human annotators for sentiment analysis. The novel approach presented in this work relies on the rich morphology of Arabic, synonymy lists, syntactical or grammatical rules, and negation rules to generate new sentences from the seed sentences with their proper labels. Most augmentation techniques target image or video data. This study is the first work to target text augmentation for Arabic language. Using this framework, we were able to increase the size of the initial seed datasets by 10 folds. Experiments that assess the impact of this augmentation on sentiment analysis showed a 42% average increase in accuracy, due to the reliability and the high quality of the rules used to build this framework.

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Negation-Aware Framework for Sentiment Analysis in Arabic Reviews

Cited by 14 publications

References 18 publications

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia during the COVID-19 Pandemic

Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis

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