Titlebook: A Practical Guide to Sentiment Analysis; Erik Cambria,Dipankar Das,Antonio Feraco Book 2017 Springer International Publishing AG 2017 Sent

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https://doi.org/10.1007/3-540-33278-2 identifying the words or phrases in a given fragment of text in which the reader understands that the author expresses some person’s positive, negative, or perhaps neutral attitude toward a topic. These four elements—expression words, attitude holder, topic, and attitude value—have evolved with har

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https://doi.org/10.1007/3-540-33278-2ss the globe. Application of automated text summarization techniques on the large volume of information piled up in social media can produce textual summaries in a variety of flavors depending on the difficulty of the use case. This chapter talks about the available set of techniques to generate sum

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https://doi.org/10.1007/3-540-33278-2onsense concepts and encode this in a semantic-aware representation. In particular, SenticNet uses dimensionality reduction to calculate the affective valence of multi-word expressions and, hence, represent it in a machine-accessible and machine-processable format. This chapter presents an overview

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