This paper addresses the problem of classifying news headlines into sentiment categories. Using a supervised approach, we train a classifier for classifying each news headline as positive, negative, or neutral. A news headline is considered positive if it is associated with good things, negative if it is associated with bad things, and neutral in the remaining cases. The experiments show an accuracy that ranges from 59.00% to 63.50% when syntactic features (argument1-verb-argument2 relations) are combined with other features. The accuracy ranges from 57.50% to 62.5% when these relations are not used.
|Journal||International Journal of Software Engineering and Its Applications|
|Publication status||Published - 2015|
- News headlines
- Sentiment classification