微網誌之短文情緒偵測: 使用時間語境, 社交, 與回應資訊

Abstract

Micro-blog is a popular social platform recently, people shares their life, or comment about something, and all of this contain vast amount of sentiment, it’s a good source we can use to analyze about the feeling of people, like what’s the feeling of people about the new product, is positive or negative. Therefore, sentiment detection is more useful in micro-blog platform, but due to the length constraint, the maximum length of post in micro-blog is only 140 characters, there is not much information than other text genres. So we exploit the property of micro-blog platform to find more information to aid the sentiment detection of post in micro-blog. We focus on three aspects: (a) context, (b) social, (c) response, and propose three approaches, i.e., Feature engineering Based, Graphical model Based, and Markov-transition based , that can exploit the information from the three aspects. Meanwhile, for the purpose of improving the sentiment detection component of Memetube system (original Pusic [1]), which is a platform that can musicalize the sentiment of micro-blogging messages for a given query, based on six basic emotion, so we focus on the six emotion (anger, surprise, sadness, disgust, fear, joy) (Paul Ekman, 1992 [7]), it’s more challenging than positive and negative sentiment.