UMBC ebiquity
Dynamic Domain Adapting Sentiment Classifiers
Tuesday, September 22, 2009, 10:15am - Tuesday, September 22, 2009, 11:30am
ITE 325 B
Justin Martineau will give us a
preview of his dissertation proposal.
Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. However, sentiment is highly domain dependent. This is particularly troubling given the scale and variety of topics seen on the web. Providing sentiment search on the web requires more than the standard machine learning approach. In this talk I describe a plan to overcome domain dependence by breaking down documents into three different types of signals. The different processing requirements exhibited by each of these signals necessitates a dynamic domain adapting approach.
Participate remotely via dimdim. After 10:15, click on JOIN MEETING and enter 'ebiquity' for the meeting name.
Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. However, sentiment is highly domain dependent. This is particularly troubling given the scale and variety of topics seen on the web. Providing sentiment search on the web requires more than the standard machine learning approach. In this talk I describe a plan to overcome domain dependence by breaking down documents into three different types of signals. The different processing requirements exhibited by each of these signals necessitates a dynamic domain adapting approach.
Participate remotely via dimdim. After 10:15, click on JOIN MEETING and enter 'ebiquity' for the meeting name.