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Traditionally, sentiment classification uses models trained on example phrases that are coded for desired sentiment. When constrained to a corpus of well-constrained utterances, such as product reviews on a website, this approach works well. We argue sentiment classification for less constrained corpora can be improved by considering context. Context can simply be the current location of a person, or as complex as knowing the person is at work after a longer than usual day. Context and sentiment classification can be combined in two ways. First, contextual information can improve sentiment classification of text. Information such as where a person was when they created the text could help interpret the content or sentiment behind the text, particularly with content that might be sarcastic or ironic. Second, we can apply sentiment classification techniques to contextual data streams to identify the sentiment of a person at a point in time.For example, knowing that a person had a fairly busy day after not sleeping well could identify that person as tired or grumpy. This can be derived from information such as a wearable sleep sensor and calendar information, activity sensors, or location information, all of which can be derived from sensors on a mobile phone.
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Contextual Sensing
and Sentiment Classification
Adrienne Andrew, Ph.D.Sentiment Symposium
March 4, 2014
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