Determining Mood for a Blog by Combining Multiple Sources of Evidence

Abstract

Mood classification for blogs is useful in helping user-to-agent interaction for a variety of applications involving the web, such as user modeling, recommendation systems, and user interface fields. It is challenging at the same time because of the diversity of the characteristics of bloggers, their experiences, and the way moods are expressed. As an attempt to handle the diversity, we combine multiple sources of evidence for a mood type. Support vector machine based mood classifier (SVMMC) is integrated with mood flow analyzer (MFA) that incorporates commonsense knowledge obtained from the general public (i.e. ConceptNet), the affective norms english words (ANEW) list, and mood transitions. In combining the two different approaches, we employ a statistically weighted voting scheme based on the support vector machine (SVM). For evaluation, we have built a mood corpus consisting of manually annotated blogs, which amounts to over 4000 blogs. Our proposed method outperforms SVMMC by 5.68% in precision. The improvement is attributed to the strategy of choosing more trustable classification results in an interleaving fashion between the SVMMC and our MFA.