Tweet the debates: understanding community annotation of uncollected sources
WSM '09 Proceedings of the first SIGMM workshop on Social media
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
From bias to opinion: a transfer-learning approach to real-time sentiment analysis
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Twitter polarity classification with label propagation over lexical links and the follower graph
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
The utility of social and topical factors in anticipating repliers in Twitter conversations
Proceedings of the 5th Annual ACM Web Science Conference
Opinion Bias Detection with Social Preference Learning in Social Data
International Journal on Semantic Web & Information Systems
A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content
Knowledge-Based Systems
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Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. "Apple product") as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.