A review of sentiment analysis research in Arabic language (2025)

Arabic Sentiment Analysis: An Empirical Study of Machine Translation's Impact

2018

The largest amount of Sentiment Analysis has been carried out for English language. To deal with Arabic sentiment analysis, machine translation of English resources or Arabic texts may be applied to built Arabic sentiment analysis systems. In this paper, we translate Arabic dataset into English and study the impact of machine translation while considering a standard Arabic system as a baseline. Experiments show that sentiment analysis of Arabic content translated into English reach a competitive performance with respect to standard sentiment analysis of Arabic texts. This suggests that machine translation can successfully transfer the expression of sentiment or polarity. Moreover , we explored the multi-domain extending of training data in order to enhance performance and we show that we should have, in the training set, data whose domain is the same as the domain of evaluation dataset.

A Review on Challenging Issues in Arabic Sentiment Analysis

Journal of Computer Science

Understanding what people think about an idea or how they evaluate a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis is the process of automatically identifying opinions expressed in text on certain subjects. The accuracy of sentiment analysis has a direct effect on decision making in both business and government. Working with the Arabic language is very important because of the growing number of online contents in Arabic and the existing resources are limited and the accuracy of existing methods is low. In this study, we do a survey to highlight Arabic sentiment analysis challenging issues based on two main perspectives: Arabic-specific and general linguistic issues. The Arabic-specific challenges are mainly caused by Arabic morphological complexity, limited resources and dialects, while the general linguistic issues include polarity fuzziness, polarity strength, implicit sentiment, sarcasm, spam, review quality and domain dependence.

Arabic Sentiment Analysis: Reviews Of The Effective Used Algorithms

Istanbul University - DergiPark, 2022

Sentiment analysis (SA) attracted many researchers due to its success in many areas such as marketing, health, and politics. It is a science of artificial intelligence (AI) and natural language processing (NLP), which aims to study people's thoughts, attitudes, and aspirations on a subject. SA is based on textual data obtained from internet sites such as electronic stores, flight and hotel reservation sites, and social media sites like Twitter and Facebook. However, the problem with that data is that it is unstructured and unorganized. The researchers had to work on organizing it using NLP tools to deal with it and analyze the feelings extracted from this data. Due to the grammatical and morphological complexity of the Arabic language and the lack of an Arabic corpus, it is still in the early phases of processing Arabic texts compared to English texts. As a result, in this research, we examined the most recent literature and scientific studies on Arabic sentiment analysis (ASA) to identify the most important algorithms that have demonstrated their quality and usefulness in this sector. We observed the researchers' interest in the use of deep learning algorithms (DL), which demonstrated their efficacy in this field and the employment of a variety of text extraction approaches, the most prominent of which were the TF-IDF CBOW and Skip-gram.

Arabic-English Sentiment Analysis-An Empirical Studypaper ID 27

The Web 2.0 refers to the second generation of World Wide Web (WWW). Web 2.0 allows Internet users to collaborate and share information online, and therefore create large virtual societies. Web 2.0 includes social network sites, Wikis, Blogs, Web services, podcasting, Multimedia sharing services ...etc. Arab users of social network sites (Facebook and Twitter) generate daily a large volume of Arabic and English textual reviews related to different social, political and scientific subjects. These reviews could be about different products, political events, sport teams, economics, video clips, restaurants, books, actors/actress, new films and songs, universities ...etc. This large volume of different Arabic and English textual reviews cannot be analyzed manually. Therefore sentiment analysis is used to identify sentiments with their subjectivity from this huge volume of reviews. In order to conduct this study a small dataset consisting of 4,050 Arabic and English reviews were collected. Three polarity dictionaries were also created (Arabic, English, and Emoticons). The collected dataset and those dictionaries were used to conduct a comparison between two free online sentiment analysis tools (SocialMention

A Survey of Sentiment Analysis in the Arabic Language

Journal of Digital Information Management, 2022

What the others think?" is an essential question for individuals, companies, and governments. All need to know the public opinions to make their decisions wisely. In the last decade, sentiment analysis and opinion mining have become one of the growing research areas. This paper presents the current state of sentiment analysis and opinion mining research. In particular, researches those deals with the Arabic language. We tried to cover the techniques and methods in sentiment analysis and the challenges in the field. We described the leading methods and approaches that have been introduced in the literature for Arabic Sentiment analysis and opinion mining. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles about Arabic Sentiment analysis. These articles are categorized according to their contributions in the various sentiment analysis techniques.

Arabic Sentiment Analysis: A Survey

International Journal of Advanced Computer Science and Applications, 2015

Most social media commentary in the Arabic language space is made using unstructured non-grammatical slang Arabic language, presenting complex challenges for sentiment analysis and opinion extraction of online commentary and micro blogging data in this important domain. This paper provides a comprehensive analysis of the important research works in the field of Arabic sentiment analysis. An in-depth qualitative analysis of the various features of the research works is carried out and a summary of objective findings is presented. We used smoothness analysis to evaluate the percentage error in the performance scores reported in the studies from their linearly-projected values (smoothness) which is an estimate of the influence of the different approaches used by the authors on the performance scores obtained. To solve a bounding issue with the data as it was reported, we modified existing logarithmic smoothing technique and applied it to pre-process the performance scores before the analysis. Our results from the analysis have been reported and interpreted for the various performance parameters: accuracy, precision, recall and F-score.

Sentence-level Arabic sentiment analysis

2012 International Conference on Collaboration Technologies and Systems (CTS), 2012

Arabic sentiment analysis research existing currently is very limited. While sentiment analysis has many applications in English, the Arabic language is still recognizing its early steps in this field. In this paper, we show an application on Arabic sentiment analysis by implementing a sentiment classification for Arabic tweets. The retrieved tweets are analyzed to provide their sentiments polarity (positive, or negative). Since, this data is collected from the social network Twitter; it has its importance for the Middle East region, which mostly speaks Arabic.

A review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

IEEE Access

Due to the significant use of Arabic language in social media networks, the demand for Arabic sentiment analysis has increased rapidly. Although, numerous sentiment analysis techniques enable people to obtain valuable insights from the opinions shared on social media. However, these techniques are still in their infancy, and the Arabic sentiment analysis domain lacks a compressive survey. Therefore, this study focused on the various characteristics, State-of-the-Art, and the level of sentiment analysis along with the natural language processing applied in the Arabic sentiment analysis. Furthermore, this study also discussed the sentiment analysis of the modern standards and the dialects of Arabic languages along with various machine learning processes and a few popular algorithms. Moreover, this study adds values by critical analysis of two case studies, which displayed an extensive set of the various research communities in this field of sentiment analysis. Finally, open research challenges are investigated, with a focus on the shortage of lexicons; availability; use of Dialect Arabic (DA); lack of corpora and datasets; right to left reading and compound phrases and idioms.

Arabic senti-lexicon: Constructing publicly available language resources for Arabic sentiment analysis

Journal of Information Science, 2017

Sentiment analysis is held to be one of the highly dynamic recent research fields in Natural Language Processing, facilitated by the quickly growing volume of Web opinion data. Most of the approaches in this field are focused on English due to the lack of sentiment resources in other languages such as the Arabic language and its large variety of dialects. In most sentiment analysis applications, good sentiment resources play a critical role. Based on that, in this article, several publicly available sentiment analysis resources for Arabic are introduced. This article introduces the Arabic senti-lexicon, a list of 3880 positive and negative synsets annotated with their part of speech, polarity scores, dialects synsets and inflected forms. This article also presents a Multi-domain Arabic Sentiment Corpus (MASC) with a size of 8860 positive and negative reviews from different domains. In this article, an in-depth study has been conducted on five types of feature sets for exploiting effective features and investigating their effect on performance of Arabic sentiment analysis. The aim is to assess the quality of the developed language resources and to integrate different feature sets and classification algorithms to synthesise a more accurate sentiment analysis method. The Arabic senti-lexicon is used for generating feature vectors. Five wellknown machine learning algorithms: naïve Bayes, k-nearest neighbours, support vector machines (SVMs), logistic linear regression and neural network are employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on standard Arabic data sets were conducted, discussion is presented and conclusions are drawn. The experimental results show that the Arabic senti-lexicon is a very useful resource for Arabic sentiment analysis. Moreover, results show that classifiers which are trained on feature vectors derived from the corpus using the Arabic sentiment lexicon are more accurate than classifiers trained using the raw corpus.

Arabic / English Sentiment Analysis: An Empirical Study

The Web 2.0 refers to the second generation of World Wide Web (WWW). Web 2.0 allows Internet users to collaborate and share information online, and therefore create large virtual societies. Web 2.0 includes social network sites, Wikis, Blogs, Web services, podcasting, Multimedia sharing services ...etc. Arab users of social network sites (Facebook and Twitter) generate daily a large volume of Arabic and English textual reviews related to different social, political and scientific subjects. These reviews could be about different products, political events, sport teams, economics, video clips, restaurants, books, actors/actress, new films and songs, universities ...etc. This large volume of different Arabic and English textual reviews cannot be analyzed manually. Therefore sentiment analysis is used to identify sentiments with their subjectivity from this huge volume of reviews. In order to conduct this study a small dataset consisting of 4,050 Arabic and English reviews were collected. Three polarity dictionaries were also created (Arabic, English, and Emoticons). The collected dataset and those dictionaries were used to conduct a comparison between two free online sentiment analysis tools (SocialMention