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Subjectivity and sentiment analysis of arabic: A survey
- In Advanced Machine Learning Technologies and Applications
, 2012
"... Abstract. Subjectivity and sentiment analysis (SSA) has recently gained consid-erable attention, but most of the resources and systems built so far are tailored to English and other Indo-European languages. The need for designing systems for other languages is increasing, especially as blogging and ..."
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Abstract. Subjectivity and sentiment analysis (SSA) has recently gained consid-erable attention, but most of the resources and systems built so far are tailored to English and other Indo-European languages. The need for designing systems for other languages is increasing, especially as blogging and micro-blogging web-sites become popular throughout the world. This paper surveys different tech-niques for SSA for Arabic. After a brief synopsis about Arabic, we describe the main existing techniques and test corpora for Arabic SSA that have been intro-duced in the literature. 1
SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media
- Proceedings of the 3rd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis,19-28
, 2012
"... Abstract In this work, we present SAMAR, a system for Subjectivity and Sentiment Analysis (SSA) for Arabic social media genres. We investigate: how to best represent lexical information; whether standard features are useful; how to treat Arabic dialects; and, whether genre specific features have a ..."
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Abstract In this work, we present SAMAR, a system for Subjectivity and Sentiment Analysis (SSA) for Arabic social media genres. We investigate: how to best represent lexical information; whether standard features are useful; how to treat Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results suggest that we need individualized solutions for each domain and task, but that lemmatization is a feature in all the best approaches.
Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms
"... Document sentiment classification is of-ten processed by applying machine learn-ing techniques, in particular supervised learning which consists basically of two major steps: feature extraction and train-ing the learning model. In the literature, most existing researches rely on n-grams as selected ..."
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Document sentiment classification is of-ten processed by applying machine learn-ing techniques, in particular supervised learning which consists basically of two major steps: feature extraction and train-ing the learning model. In the literature, most existing researches rely on n-grams as selected features, and on a simple basic classifier as learning model. In the context of our work, we try to improve document classification findings in Ara-bic sentiment analysis by combining dif-ferent types of features such as opinion and discourse features; and by proposing an ensemble-based classifier to investi-gate its contribution in Arabic sentiment classification. Obtained results attained 85.06 % in terms of macro-averaged F-measure, and showed that discourse fea-tures have moderately improved F-measure by approximately 3 % or 4%. 1
Arabic Sentiment Analysis: A Survey
"... Abstract—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 ..."
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Abstract—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
ASTD: Arabic Sentiment Tweets Dataset
"... This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. We present the properties and the statistics of the dataset, and run ex ..."
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This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. We present the properties and the statistics of the dataset, and run experiments using standard par-titioning of the dataset. Our experiments provide benchmark results for 4 way sen-timent classification on the dataset. 1
1LABR: A Large Scale Arabic Book Reviews
"... Abstract—We introduce LABR, the largest sentiment analysis dataset to-date for the Arabic language. It consists of over 63,000 book reviews, each rated on a scale of 1 to 5 stars. We investigate the properties of the the dataset, and present its statistics. We explore using the dataset for two tasks ..."
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Abstract—We introduce LABR, the largest sentiment analysis dataset to-date for the Arabic language. It consists of over 63,000 book reviews, each rated on a scale of 1 to 5 stars. We investigate the properties of the the dataset, and present its statistics. We explore using the dataset for two tasks: sentiment polarity classification and ratings classification. We provide standard splits of the dataset into training, validation and testing, for both polarity and ratings classification, in both balanced and unbalanced settings. We extend the work done in Aly and Atiya [2013] by performing a comprehensive analysis on the dataset. In particular, we perform an extended survey of the different classifiers typically used for the sentiment polarity classification problem. Also we construct a sentiment lexicon from the dataset that contains both single and compound sentiment words and we explore its effectiveness. I.
Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews
"... Abstract—Sentiment Analysis (SA) is one of hottest fields in data mining (DM) and natural language processing (NLP). The goal of SA is to extract the sentiment conveyed in a certain text based on its content. While most current works focus on the simple problem of determining whether the sentiment i ..."
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Abstract—Sentiment Analysis (SA) is one of hottest fields in data mining (DM) and natural language processing (NLP). The goal of SA is to extract the sentiment conveyed in a certain text based on its content. While most current works focus on the simple problem of determining whether the sentiment is positive or negative, Multi-Way Sentiment Analysis (MWSA) focuses on sentiments conveyed through a rating or scoring system (e.g., a 5-star scoring system). In such scoring systems, the sentiments conveyed in two reviews of close scores (such as 4 stars and 5 stars) can be very similar creating an added challenge compared to traditional SA. One intuitive way of handling this challenge is via a divide-and-conquer approach where the MWSA problem is divided into a set of sub-problems allowing the use of customized classifiers to differentiate between reviews of close scores. A hierarchical classification structure can be used with
Using Natural Language Processing to Mine Multiple Perspectives from Social Media and Scientific Literature
, 2013
"... To my mother, my wife, and my adorable daughters ii ACKNOWLEDGEMENTS First and foremost, all praise and thanks are due to God for giving me the power to believe in my passion and pursue my dreams. I could never have finished this dissertation without the faith I have in Him. I am heartily thankful t ..."
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To my mother, my wife, and my adorable daughters ii ACKNOWLEDGEMENTS First and foremost, all praise and thanks are due to God for giving me the power to believe in my passion and pursue my dreams. I could never have finished this dissertation without the faith I have in Him. I am heartily thankful to my advisor, Professor Dragomir Radev for his splendid guidance and encouragement during the course of my studies. His support and insightful advice were crucial to my academic success and my development as a researcher. I am also grateful to Steven Abney, Eytan Adar, Qiaozhu Mei, and Emily Provost for serving on my dissertation committee and for providing constructive comments that made this dissertation better. I was fortunate to collaborate and discuss research with brilliant researchers at the Computational Linguistics and Information Retrieval (CLAIR) group at the univer-
