Feature Engineering for Sentiment

Start: Tuesday, November 18, 2008, 10:30AM

End: Tuesday, November 30, 1999, 12:00AM

Location: 325b ITE

Abstract: Sentiment analysis upon free text is a difficult domain since free text is often informally written, poorly structured, and rife with spelling and grammatical errors. These characteristics make them difficult to parse and process with standard language analysis tools. These factors have made machine learning techniques such as bag of words support vector machines very popular. We describe a better feature space to use with support vector machines that relies upon the uneven distribution of words commonly seen in polar text.


	UMBC eBiquity Events / Talks Feature Engineering for Sentiment OWL metadata Update record		Feature Engineering for Sentiment Tweet Speaker: Justin Martineau Start: Tuesday, November 18, 2008, 10:30AM End: Tuesday, November 30, 1999, 12:00AM Location: 325b ITE Abstract: Sentiment analysis upon free text is a difficult domain since free text is often informally written, poorly structured, and rife with spelling and grammatical errors. These characteristics make them difficult to parse and process with standard language analysis tools. These factors have made machine learning techniques such as bag of words support vector machines very popular. We describe a better feature space to use with support vector machines that relies upon the uneven distribution of words commonly seen in polar text. ,