Twitter Benchmark Dataset for Arabic Sentiment Analysis

Gamal, Donia; Alfonse, Marco; El-Horbaty, El-Sayed M.; Salem, Abdel-Badeeh M.

doi:10.5815/ijmecs.2019.01.04

Cited by 35 publications

(26 citation statements)

References 9 publications

(5 reference statements)

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“…Several studies have been conducted using automatic techniques for corpus construction and annotation. For this purpose, three main techniques have been used: (1) automatic annotation based on rating reviews [10][11][12][13][14], (2) sentiment lexicon [15][16][17][18], and (3) external application programming interfaces (APIs) [19]. In the context of sentiment annotation based on rating reviews, a Large-Scale Arabic Book Reviews (LABR) corpus was proposed in [10] for a specific domain.…”

Section: Sentiment Annotationmentioning

confidence: 99%

“…Based on the created sentiment lexicon, a corpus containing 8000 messages, which were written in Arabic and Arabizi (writing Arabic using English characters), was automatically annotated into positive and negative classes. Another sentiment corpus was proposed in [16]. It contains 151,548 tweets in modern standard Arabic (MSA) and Egyptian dialects.…”

Section: Sentiment Annotationmentioning

confidence: 99%

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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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Section: Sentiment Annotationmentioning

confidence: 99%

Section: Sentiment Annotationmentioning

confidence: 99%

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

et al. 2021

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“…However, relying on emoticons only leads to many errors where some users express a contradictory sentiment between the text and the emotions that they used. More recently, Gamal et al [25] presented a large sentiment corpus dedicated to MSA and Egyptian dialect. They also relied on a sentiment lexicon for the automatic annotation.…”

Section: Arab(ic+izi) Sentiment Analysis: Challengesmentioning

confidence: 99%

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

et al. 2021

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In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).

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“…Alsolamy et al built manually corpus-based sentiment lexicon for Arabic opinion Mining [7]. Furthermore, Gamal et al constructed a benchmark dataset of Arabic Dialect Tweets [14]. Zaghouani et al built Annotation Procedure that consists annotation management and MT post-editing annotation for Modern Standard Arabic corpora Machine Translation.…”

Section: Related Workmentioning

confidence: 99%

ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

Mohamed¹,

Hussien²,

Mousa³

2021

IJITCS

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There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.

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Twitter Benchmark Dataset for Arabic Sentiment Analysis

Cited by 35 publications

References 9 publications

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

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

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

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