Product Sentiment analysis using support vector
machines based on opinion-based texts
Abstract: There
are a number of challenging aspects in recognizing the favorability of
opinion-based texts, the task known as sentiment analysis. Opinions in natural
language are very often expressed in subtle and complex ways, presenting
challenges which may not be easily addressed by simple text categorization
approaches such as n-gram or keyword identification approaches. Although such
approaches have been employed effectively, there appears to remain considerable
room for improvement. Moving beyond these approaches can involve addressing the
task at several levels. Negative reviews may contain many apparently positive
phrases even while maintaining a strongly negative tone,
and the opposite is also common.
This
presentation will concentrate on the implementation methods of SVM.