ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Tracking point of view in narrative
Computational Linguistics
Recognizing subjectivity: a case study in manual tagging
Natural Language Engineering
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Statistical parsing of morphologically rich languages (SPMRL): what, how and whither
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
Discovering K web user groups with specific aspect interests
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Analyzing Urdu social media for sentiments using transfer learning with controlled translations
LSM '12 Proceedings of the Second Workshop on Language in Social Media
SAMAR: a system for subjectivity and sentiment analysis of Arabic social media
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
SAMAR: Subjectivity and sentiment analysis for Arabic social media
Computer Speech and Language
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Although Subjectivity and Sentiment Analysis (SSA) has been witnessing a flurry of novel research, there are few attempts to build SSA systems for Morphologically-Rich Languages (MRL). In the current study, we report efforts to partially fill this gap. We present a newly developed manually annotated corpus of Modern Standard Arabic (MSA) together with a new polarity lexicon. The corpus is a collection of newswire documents annotated on the sentence level. We also describe an automatic SSA tagging system that exploits the annotated data. We investigate the impact of different levels of preprocessing settings on the SSA classification task. We show that by explicitly accounting for the rich morphology the system is able to achieve significantly higher levels of performance.