Exploring the Effects of Word Roots for Arabic Sentiment Analysis

International Joint Conference on Natural Language Processing, pages 471–479, Nagoya, Japan, 14-18 October 2013. Exploring the Effects of Word Roots for Arabic Sentiment Analysis Shereen M. Oraby College of Engineering and Technology AAST, Alexandria, Egypt shereen.oraby@aast.edu Yasser El-Sonbaty College of Computing and Information Technology AAST, Alexandria, Egypt yasser@aast.edu Mohamad Abou El-Nasr College of Engineering and Technology AAST, Alexandria, Egypt mnasr@aast.edu Abstract The inherent morphological complexity of languages such as Arabic entails the ex- ploration of language traits that could be valuable to the task of detecting and clas- sifying sentiment within text. This pa- per investigates the relevance of using the roots of words as input features into a sentiment analysis system under two dis- tinct domains, in order to tailor the task more suitably to morphologically-rich lan- guages such as Arabic. Different word- rooting solutions are employed in conjunc- tion with a basic sentiment classifier, in or- der to demonstrate the potential of map- ping Arabic words to basic roots for a language-specific development to the sen- timent analysis task, showing a notewor- thy improvement to baseline performance. 1 Introduction An increasing need for quick and effective analy- sis of huge masses of text has sparked a revolution in the requirements of natural language process- ing systems, demanding an ability to handle varied types and formats of textual data for a wide range of language analysis tasks, both on the syntactic and semantic levels. The task of sentiment anal- ysis in particular presents a unique form of text analytics due to the flourish of new opinionated web data in social media and otherwise, dealing with the detection and of opinions within a text, and then further with distinguishing their polarity. Two main tasks are of great importance with respect to the classification of opinions in text, regardless of the language under inspection: the tasks of subjectivity detection in a set of statements to differentiate between purely objective reporting of information in the form of facts, as opposed to a subjective account of the information; and the task of sentiment analysis, which entails classify- ing the resultant subjective statements into a set of classes, positive, negative, and neutral, depending on the polarity of the opinion expressed. With re- spect to the level of analysis performed, individual tasks may be more relevant than others: while sub- jectivity analysis is relevant at the sentence level to sort out opinionated statements, sentiment anal- ysis can be appropriate at both the sentence and the document level, if the excerpts are already de- fined to be subjective, and the task is to distinguish the polarity of the opinion being expressed (Liu, 2012). While much research has been attributed to the task of sentiment analysis in English, fewer at- tempts tackle the task in other more morpholog- ically complex languages such as Arabic, and in- creasing amounts of information available in these languages makes the task of Arabic sentiment analysis a very relevant one, albeit a challenge for classification systems. Such language processing tasks are made …

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