# AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus
## 2 Related Work
### Sentiment Annotation  
Arabic text can be classified into three categories: (a) classical Arabic, the version in which the Quran (the holy book of Islam) is written in, (b) modern standard Arabic (MSA), which is used in all Arabic countries for official and formal purposes such as newspapers, schools, and universities, and (c) dialectical Arabic (DA), which is used in everyday life among the people in different regions. For sentiment analysis, most of the existing textual corpora are either MSA or DA. The sentiment annotation of Arabic text can be classified into three approaches: (1) automatic annotation, (2) semi-automatic annotation, and (3) manual annotation. These three annotation approaches are summarized in Figure 1. 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. This corpus is a collection of book reviews containing 63,257 book reviews that had been written by readers. Each review is on a scale from 1 to 5 based on the user's rating of books. The authors considered reviews with a score of 1 or 2 as negative, 4 or 5 as positive, and 3 as neutral. Another sentiment corpus is called \"BRAD 1.0\", a large sentiment corpus that was created in [12] and motivated by the LABR corpus in [10]. This corpus consists of 510,600 book reviews. It was collected from the goodreader.com website. The user's rating of each book review on a scale of 1-5 was used to annotate the corpus reviews by following [10]. Along with modern standard Arabic, the corpus also includes some dialects such as Egyptian dialect content. BRAD 2.0 is an extension of BRAD 1.0. It contains more than 200K extra reviews to account for more Arabic dialects [11]. To classify the reviews, they followed the same procedures as in [10; 12].User ratings are used to annotate each review in the corpus into positive, negative, or neutral classes. Similarly, among the presented corpora in [12; 14], HARD is the most recent sentiment corpus for hotel reviews [14]. This corpus consists of more than 370,000 reviews. It was collected from the booking.com website and automatically annotated based on the reviews rating, but on a scale of 1-10. Each review is annotatedas positive, negative, or neutral based on the user's rating of the review. Similar work was done in [11]. The authors annotated a collection of reviews from different domains such as hotels, restaurants, movies, and product reviews. The reviews were extracted from different websites. The annotated corpus contains 33,000 reviews. The second approach of the automatic annotation is the use of sentiment lexicons to automatically annotate the textual corpora. In [15], an Algerian sentiment lexicon was automatically constructed by translating English sentiment lexicons. 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. The tweets were classified into positive and negative classes. They relied on the corpus proposed in [10] by manually extracting and annotating a list of 4404 phrases that are commonly used in expressing sentiment, to be used in the annotation of their corpus. The annotation was performed based on the manually annotated phrases and the frequency of positive and negative words appearing in the text. Another work was done in [20] by collecting 10 million tweets from Twitter and using a list of positive and negative Arabic words as search keywords.