Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Introduction to Information Retrieval
Introduction to Information Retrieval
Summarization system evaluation revisited: N-gram graphs
ACM Transactions on Speech and Language Processing (TSLP)
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
We feel fine and searching the emotional web
Proceedings of the fourth ACM international conference on Web search and data mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
WSM2011: third ACM workshop on social media
MM '11 Proceedings of the 19th ACM international conference on Multimedia
VideoSkip: event detection in social web videos with an implicit user heuristic
Multimedia Tools and Applications
Mining movie archives for song sequences
Multimedia Tools and Applications
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Sentiment Analysis over Social Media facilitates the extraction of useful conclusions about the average public opinion on a variety of topics, but poses serious technical challenges. This is because of the sparse, noisy, multilingual content that is posted on-line by Social Media users. In this paper, we introduce a novel method for capturing textual patterns that inherently supports this challenging type of content. In essence, it creates a graph whose nodes correspond to the character n-grams of a document, while its weighted edges denote the average distance between them. Multiple documents of the same polarity can be aggregated into a polarity class graph, which can be compared with individual documents in order to identify the category of their sentiment. To evaluate our approach, we conducted large scale experiments on a real-world data set stemming from a snapshot of Twitter activity. The outcomes of our evaluation indicate significant improvements over other the methods typically used in this context, not only with respect to effectiveness, but also to efficiency.