VOXSUP: a social engagement framework
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A generic approach to generate opinion lists of phrases for opinion mining applications
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Crowdsourcing recommendations from social sentiment
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Probabilistic macro behavioral targeting
Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Accurate energy expenditure estimation using smartphone sensors
Proceedings of the 4th Conference on Wireless Health
Detecting and tracking disease outbreaks by mining social media data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Social Media is becoming major and popular technological platform that allows users discussing and sharing information. Information is generated and managed through either computer or mobile devices by one person and consumed by many other persons. Most of these user generated content are textual information, as Social Networks(Face book, Linked In), Microblogging(Twitter), blogs(Blogspot, Word press). Looking for valuable nuggets of knowledge, such as capturing and summarizing sentiments from these huge amount of data could help users make informed decisions. In this paper, we develop a sentiment identification system called SES which implements three different sentiment identification algorithms. We augment basic compositional semantic rules in the first algorithm. In the second algorithm, we think sentiment should not be simply classified as positive, negative, and objective but a continuous score to reflect sentiment degree. All word scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, we propose a third algorithm which takes emoticons, negation word position, and domain-specific words into account. Furthermore, a machine learning model is employed on features derived from outputs of three algorithms. We conduct our experiments on user comments from Face book and tweets from twitter. The results show that utilizing Random Forest will acquire a better accuracy than decision tree, neural network, and logistic regression. We also propose a flexible way to represent document sentiment based on sentiments of each sentence contained. SES is available online.