Sentiment analysis (SA) is a continuing field of research that lies at the intersection of many fields such as data mining, natural language processing and machine learning It is concerned with the automatic extraction of opinions conveyed in a certain text Due to its vast applications, many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention This survey presents a comprehensive overview of the works done so far on Arabic SA (ASA) The survey groups published papers based on the SA-related problems they address and tries to identify the gaps in the current literature laying foundation for future studies in this field

A comprehensive survey of arabic sentiment analysis

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

A Survey of Opinion Mining in Arabic: A Comprehensive System Perspective Covering Challenges and Advances in Tools, Resources, Models, Applications, and Visualizations

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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Arabic Sentiment Analysis: A Systematic Literature Review

Tweet categorization by combining content and structural knowledge

Sentiment analysis is the field of natural language processing to analyze opinionated data, for the purpose of decision making. An opinion is a statement about a subject which expresses the sentiments as well as the emotions of the opinion makers on the topic. In this paper, we develop a sentiment analysis tool namely SENTI-METER. This tool estimates the success rate of social campaigns based on the algorithms we developed that analyze the sentiment of word as well as blog. Social campaigns have a huge impact on the mindset of people. One such campaign was launched in India on October 2, 2014, named Swachh Bharat Abhiyan (SBA). Our tool computes an elaborated analysis of Swachh Bharat Abhiyan, which examines the success rate of this social campaign. Here, we performed the location-wise analysis of the campaign and predict the degree of polarity of tweets along with the monthly and weekly analysis of the tweets. The experiments were conducted in five phases namely extraction and preprocessing of tweets, tokenization, sentiment evaluation of a line, sentiment evaluation of a blog (document) and analysis. Our tool is also capable of handling transliterated words. Unbiased tweets were extracted from Twitter related to this specific campaign, and on comparing with manual tagging we were able to achieve 84.47 % accuracy using unigram machine learning approach. This approach helps the government to implement the social campaigns effectively for the betterment of the society.

Sentiment analysis on social campaign “Swachh Bharat Abhiyan” using unigram method

In recent years, there is enormous increase of data content due to emergence of social media platforms in digital word of internet. The text mining is very important technique to discover the knowledge from unstructured data. Automatic sentiment analysis is one of the important applications of text mining. The sentiment analysis is used to predict the text polarity (positive, negative, and neutral). Furthermore, the most of users using social media such as Twitter use informal language to express their opinions. In this paper, we propose an automatic approach to predict sentiments for informal Arabic language. We chose Arabic tweets as input for our study. We observed through our experiment results that although emotion icons presence in the tweets helps in development of comparatively more accurate classifier, however they play ambiguous role in defining the sentiments of tweets.

Khan Muhammad Badruddin

Papers

Role of Emotion icons in Sentiment classification of Arabic Tweets