Discovering Semantic and Sentiment Correlations using Short Informal Arabic Language Text
نویسندگان
چکیده
منابع مشابه
Discovering semantic and sentiment correlations using huge corpus of short informal Arabic language text
Semantic and Sentiment analysis have received a great deal of attention over the last few years due to the important role they play in many different fields, including marketing, education, and politics. Social media has given tremendous opportunities for researchers to collect huge amount of data as input for their semantic and sentiment analysis. Using twitter API, we collected around 4.5 mil...
متن کاملSentiment Strength Detection in Short Informal Text
Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai Statistical Cybermetrics Research Group, School of Computing and Information Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1SB, UK. E-mail: [email protected], [email protected], [email protected], [email protected] Tel: +44 1902 321470 Fax: +44 1902 321478 Arvid Kappas School of Humanities and Social ...
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Semantic annotation is the process of adding semantic metadata to resources. Semantic metadata is data concerning the meaning of entities and the relationships that exist. Semantic annotation cannot be performed without an ontology suitable for the task. In this research paper, we describe the design, implementation, and evaluation of a lexical ontology for Arabic semantic relations. The main p...
متن کاملSentiment Analysis in the Arabic Language Using Machine Learning
Sentiment Analysis in the Arabic Language Using Machine Learning Sentiment analysis has recently become one of the growing areas of research related to natural language processing and machine learning. Much opinion and sentiment about specific topics are available online, which allows several parties such as customers, companies and even governments, to explore these opinions. The first task is...
متن کاملSentiment Analysis of Short Informal Texts
We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surfaceform, semantic, and sentiment features. Th...
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ژورنال
عنوان ژورنال: International Journal of Advanced Computer Science and Applications
سال: 2017
ISSN: 2156-5570,2158-107X
DOI: 10.14569/ijacsa.2017.080126