Sentiment Analysis and Opinion Mining

Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Nuria Bel, Salud María Jiménez-Zafra, Gülşen Eryiği…

Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet con…

Summary Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of se…

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Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amou…

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Abstract Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age‐, gender‐, race‐, or geography‐related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive pe…

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Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to present an effective sarcasm identification framework on social media data by pur…

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This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses. The claim spotting model is built using a human-labeled dataset of check-worthy factual claims from the U.S. general election debate tran…

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