Machine Learning
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Handling noisy training and testing data
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Graph-based text classification: learn from your neighbors
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Knowledge and Information Systems
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
The utility of linguistic rules in opinion mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Entity-centric topic-oriented opinion summarization in twitter
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Aspect and sentiment extraction based on information-theoretic co-clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
TWIPIX: a web magazine curated from social media
Proceedings of the 20th ACM international conference on Multimedia
Sentiment-focused web crawling
Proceedings of the 21st ACM international conference on Information and knowledge management
NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Sentiment and topic analysis on social media: a multi-task multi-label classification approach
Proceedings of the 5th Annual ACM Web Science Conference
Exploiting hybrid contexts for Tweet segmentation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
I act, therefore I judge: network sentiment dynamics based on user activity change
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Learning topical translation model for microblog hashtag suggestion
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Towards social data platform: automatic topic-focused monitor for twitter stream
Proceedings of the VLDB Endowment
Opinion Bias Detection with Social Preference Learning in Social Data
International Journal on Semantic Web & Information Systems
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Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various interesting topics, such as brands, products or celebrities, etc. Naturally, people may anticipate an approach to receiving the common sentiment tendency towards these topics directly rather than through reading the huge amount of tweets about them. On the other side, Hashtags, starting with a symbol "#" ahead of keywords or phrases, are widely used in tweets as coarse-grained topics. In this paper, instead of presenting the sentiment polarity of each tweet relevant to the topic, we focus our study on hashtag-level sentiment classification. This task aims to automatically generate the overall sentiment polarity for a given hashtag in a certain time period, which markedly differs from the conventional sentence-level and document-level sentiment analysis. Our investigation illustrates that three types of information is useful to address the task, including (1) sentiment polarity of tweets containing the hashtag; (2) hashtags co-occurrence relationship and (3) the literal meaning of hashtags. Consequently, in order to incorporate the first two types of information into a classification framework where hashtags can be classified collectively, we propose a novel graph model and investigate three approximate collective classification algorithms for inference. Going one step further, we show that the performance can be remarkably improved using an enhanced boosting classification setting in which we employ the literal meaning of hashtags as semi-supervised information. Experimental results on a real-life data set consisting of 29,195 tweets and 2,181 hashtags show the effectiveness of the proposed model and algorithms.