Machine Learning - Special issue on inductive transfer
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-task feature and kernel selection for SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Multi-task learning for boosting with application to web search ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Proceedings of the 20th ACM international conference on Information and knowledge management
Tweet classification by data compression
Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web
Twitter Trending Topic Classification
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Multilabel classification with principal label space transformation
Neural Computation
Improving tweet stream classification by detecting changes in word probability
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Community-based classification of noun phrases in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Expert Systems with Applications: An International Journal
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Both sentiment analysis and topic classification are frequently used in customer care and marketing. They can help people understand the brand perception and customer opinions from social media, such as online posts, tweets, forums, and blogs. As such, in recent years, many solutions have been proposed for both tasks. However, we believe that the following two problems have not been addressed adequately: (1) Conventional solutions usually treat the two tasks in isolation. When the two tasks are closely related (e.g., posts about "customer care" often have a "negative" tone), exploring their correlation may yield a better accuracy; (2) Each post is usually assigned with only one sentiment label and one topic label. Since social media is, compared to traditional document corpus, more noisy, ambiguous, and sparser, single label classification may not be able to capture the post classes accurately. To address these two problems, in this paper, we propose a multi-task multi-label (MTML) classification model that performs classification of both sentiments and topics concurrently. It incorporates results of each task from prior steps to promote and reinforce the other iteratively. For each task, the model is trained with multiple labels so that they can help address class ambiguity. In the empirical validation, we compare the accuracy of MTML model against four competing methods in two different settings. Results show that MTML produces a much higher accuracy of both sentiment and topic classifications.