微網誌之短文情緒偵測: 使用時間語境, 社交, 與回應資訊

Sentiment Detection of Micro-blogging Short Texts via Contextual, Social, and Responsive Information

王建元(Chien-Yuan Wang)

指導教授 ： 林守德

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2011年)

https://doi.org/10.6342/NTU.2011.00147

摘要

無資料

關鍵字

情緒偵測 ； 情緒分類

並列摘要

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.

並列關鍵字

sentiment detection ； sentiment classification

參考文獻

[1] Cheng-Te Li, Hung-Che Lai, Chien-Tung Ho, Chien-Lin Tseng, Shou-De Lin. 2010 Pusic: musicalize microblog messages for summarization and exploration WWW’10

[3] FÅ Nielsen(2011), A new ANEW: Evaluation of a word list for sentiment analysis in microblogs, In International Workshop on Making Sense of Microposts 2011

[6] Go, A.; Bhayani, R.; and Huang, L. 2009. Twitter Sentiment Classification using Distant Supervision. Technical Report, Stanford University.

[7] Ekman, P.: An Argument for Basic Emotions. Cognition and Emotion. 6, 169–200 (1992)

[9] Bermingham, A., and Smeaton, A. F. 2010. Classifying Sentiment in Microblogs: is Brevity an Advantage? In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM’10), 1183–1186.