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Yunlun Yang

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2 papers

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AAAI Conference 2016 Conference Paper

Identifying Sentiment Words Using an Optimization Model with L1 Regularization

Zhi-Hong Deng
Hongliang Yu
Yunlun Yang

Sentiment word identiﬁcation is a fundamental work in numerous applications of sentiment analysis and opinion mining, such as review mining, opinion holder ﬁnding, and twitter classiﬁcation. In this paper, we propose an optimization model with L1 regularization, called ISOMER, for identifying the sentiment words from the corpus. Our model can employ both seed words and documents with sentiment labels, different from most existing researches adopting seed words only. The L1 penalty in the objective function yields a sparse solution since most candidate words have no sentiment. The experiments on the real datasets show that ISOMER outperforms the classic approaches, and that the lexicon learned by ISOMER can be effectively adapted to document-level sentiment analysis.

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AAAI Conference 2014 Conference Paper

A Joint Optimization Model for Image Summarization Based on Image Content and Tags

Hongliang Yu
Zhi-Hong Deng
Yunlun Yang
Tao Xiong

As an effective technology for navigating a large number of images, image summarization is becoming a promising task with the rapid development of image sharing sites and social networks. Most existing summarization approaches use the visual-based features for image representation without considering tag information. In this paper, we propose a novel framework, named JOINT, which employs both image content and tag information to summarize images. Our model generates the summary images which can best reconstruct the original collection. Based on the assumption that an image with representative content should also have typical tags, we introduce a similarity-inducing regularizer to our model. Furthermore, we impose the lasso penalty on the objective function to yield a concise summary set. Extensive experiments demonstrate our model outperforms the state-of-the-art approaches.

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