Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects

Baly, Ramy; El-Khoury, Georges; Moukalled, Rawan; Aoun, Rita; Hajj, Hazem; Shaban, Khaled; El-Hajj, Wassim

doi:10.1016/j.procs.2017.10.118

Cited by 54 publications

(21 citation statements)

References 23 publications

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“…After going through the whole data, the network returns a decision about each vector in the sequence. The produced output is then used to train a softmax layer for sentiment classification [65].…”

Section: Our Approachmentioning

confidence: 99%

ASA: A framework for Arabic sentiment analysis

Oussous

Benjelloun

Lahcen

et al. 2019

Journal of Information Science

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Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

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Section: Our Approachmentioning

confidence: 99%

ASA: A framework for Arabic sentiment analysis

Oussous

Benjelloun

Lahcen

et al. 2019

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…In [9], Baly et al developed a multi-dialect Arabic twitter sentiment dataset that contains tweets from 12 Arabic countries. They compared different sentiment models on the Egyptian and Emarati datasets.…”

Section: Related Work a Twitter Sentiment Classificationmentioning

confidence: 99%

“…Given the peculiar nature of the Arabic language, where the different forms of Arabic (MSA and dialects) affect the performance of all text classification tasks [9], we evaluate the proposed models on three datasets of Arabic tweets. The first dataset is the SemEval 2017 Arabic tweet dataset [22].…”

Section: Twitter Sentiment Classification a Datasetsmentioning

confidence: 99%

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Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets

Al-Twairesh

Al-Negheimish

2019

IEEE Access

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Sentiment analysis (SA) of Arabic tweets is a complex task due to the rich morphology of the Arabic language and the informal nature of language on Twitter. Previous research on the SA of tweets mainly focused on manually extracting features from the text. Recently, neural word embeddings have been utilized as less labor-intensive representations than manual feature engineering. Most of these word-embeddings model the syntactic information of words while ignoring the sentiment context. In this paper, we propose to learn sentiment-specific word embeddings from Arabic tweets and use them in the Arabic Twitter sentiment classification. Moreover, we propose a feature ensemble model of surface and deep features. The surface features are manually extracted features, and the deep features are generic word embeddings and sentimentspecific word embeddings. The extensive experiments are performed to test the effectiveness of the surface and deep features ensemble, pooling functions, embeddings size, and cross-dataset models. The recent language representation model BERT is also evaluated on the task of SA of Arabic tweets. The models are evaluated on three different datasets of Arabic tweets, and they outperform the previous results on all these datasets with a significant increase in the F-score. The experimental results demonstrate that: 1) the highest performing model is the ensemble of surface and deep features and 2) the approach achieves the state-of-the-art results on several benchmarking datasets. INDEX TERMS Arabic sentiment analysis, arabic sentiment embeddings, arabic tweets, surface and deep features ensemble.

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“…It is a fast and excellent tool to build NLP models and generate live predictions [6]. LSTM with word embeddings is used to perform the sentiment classification, as this classifier outperforms the traditional techniques in text classification [7].The classifier performed well with embeddings, especially when dealing with the sentiment classification of Arabic dialects [8].…”

Section: Introductionmentioning

confidence: 99%

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

et al. 2021

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At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.

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Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects

Cited by 54 publications

References 23 publications

ASA: A framework for Arabic sentiment analysis

ASA: A framework for Arabic sentiment analysis

Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

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