Abstract In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a

The analysis of user opinions expressed on the Web is becoming increasingly relevant to a variety of applications. It allows us to track the evolution of opinions or discussions in the blogosphere, or perform product surveys. The aggregation of sentiments and analysis of contradictions is another

Sentiment analysis of citations in scientific papers and articles is a new and interesting problem which can open up many exciting new applications in bibliographic search and bibliometrics. Current work on citation sentiment detection focuses on only the citation sentence. In this paper, we

messaging, and demonstrate the importance of handling it correctly. We then show that lengthening is strongly associated with subjectivity and sentiment. Finally, we present an automatic method which leverages this association to detect domain-specific sentiment- and emotionbearing words. We evaluate our

of our system with different window sizes. The results show that there is no significant difference in classification accuracy when different window sizes have been applied. However, negation detection helped to identify more opinion or sentiment carrying expressions. We conclude that traditional

Abstract—Automatic detection of the sentiment of a given text is a difficult but highly relevant task. Application areas range from financial news, where information about sentiments can be used to predict stock movements, to social media, where user recommendations can determine success or failure

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Sentiment analysis is here defined as a machine learning problem to analyse human documents and extract the human opinion they convey. Kanayma et al describe the field as: ‘a task to obtain writers ’ feelings as expressed in positive or negative comments, questions, and requests, by analysing large

we focus on aspect-based summarization models [8], where a summary is built by extracting relevant aspects of a service, such as service or value, aggregating the sentiment per aspect, and selecting aspect-relevant text. We describe the details of both the aspect extraction and sentiment detection

sentiment analysis can help spammer detection in online social media. In particular, we first conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and then present an optimization formulation that incorporates sentiment information into a novel social spammer