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Sentiment Analysis of Arabic Social Networks

Sentiment Analysis of Arabic Social Networks . Presented by Eshrag Refaee. Supervisors Dr. Verena Rieser & Prof. Rob Pooley. Outline . The concept of sentiment analysis Arabic as a morphologically rich language Aims of the research Sentiment analysis in English and Arabic literature

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Sentiment Analysis of Arabic Social Networks

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  1. Sentiment Analysis of Arabic Social Networks Presented by EshragRefaee Supervisors Dr. VerenaRieser & Prof. Rob Pooley

  2. Outline • The concept of sentiment analysis • Arabic as a morphologically rich language • Aims of the research • Sentiment analysis in English and Arabic literature • Twitter corpus: collection and annotation • Empirical work • Results and evaluation • Future work

  3. Sentiment analysis • Definition: Analysing and understanding people’s sentiments, evaluations, opinions, attitudes, and emotions from written text. • Research on SA appeared early 2000 (Liu, 2012). • SA is one of the most active research areas in NLP.

  4. Applications • In addition to its significance as a major sub-field of Natural Language Processing (NLP)research, SSA has a potential of several: • Commercial applications measuring success of a product • Social applications • Political applications • Economical applications

  5. Sentiment analysis of social networks • The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, and micro-blogs. • A social network like twitter, with more than 500 million active users (ALEXA, 2012), provides a global arena for users to share views, attitudes, preferences etc; and discuss points of agreement, and/or conflict. • March 2012, Twitter has become available in Arabic (Twitter Blog, 2012)

  6. About Arabic • Arabic is the language of an aggregate population of over 300 million people, first language of the 22 member countries of the Arabic League and official language in three others (Habash, 2010).

  7. About Arabic • Arabic language can be classified into three major levels: • Classic Arabic (CA) • Modern standard Arabic (MSA) • Arabic Dialects (AD). • Social networks uses DA & MSA side-by-side(Al-Sabbagh, and Girju, 2012).

  8. Aims of this research • Construct a corpus of Arabic tweets for sentiment analysis. • Build and test classification models for automatic sentiment analysis. • Explore distant supervision approaches to build efficient models for the changing twitter stream.

  9. Approach and methodology

  10. Our Arabic Twitter corpus • (Refaee E, and Rieser V, 2014). An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis. Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014)Reykjavik, Iceland. • Corpus freely available from LREC repository.

  11. Approach and methodology

  12. Building training set :features extraction & feature vector construction Class of a new document Classifier/ learner

  13. Experimental settings • Machine learners We use the implementations of the following algorithms provided by the WEKA data mining package – version 3.7.9 (Witten and Frank, 2005). • Sequential Minimal Optimization-SMO (Platt, 1999) Support Vector Machines (SVM) • ZeroR (baseline scheme) SVM aims to identify the Optimal hyperplanethat linearly separates data instances with the maximum margin

  14. Results and evaluation 2-level classification: Subjectivevs. Objective

  15. Results and evaluation 2-level classification: positive vs. negative

  16. Current direction of research • Applying semi-supervised learning to automatically annotate the rest of our twitter corpus. • Investigate distant learning approaches to boost a large training set to be used for models’ optimisation. • Building a high quality polarity lexicon to be employed in automatically detecting/identifying the overall sentiment orientation of a given text. • Explore culture-related features that can detect cultural references in user-generated text.

  17. Thanks @eshragR

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