Kevin Hsin-Yih Lin

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We study the classification of news articles into emotions they invoke in their readers. Our work differs from previous studies, which focused on the classification of documents into their authors' emotions instead of the readers'. We use various combinations of feature sets to find the best combination for identifying the emotional influences of news(More)
Most of the common techniques in text retrieval are based on the statistical analysis of a term either as a word or a phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying representation should indicate terms that capture the semantics of text.(More)
Past studies on emotion classification focus on the writer’s emotional state. This research addresses the reader aspect instead. The classification of documents into reader-emotion categories has several applications. One of them is to integrate reader-emotion classification into a web search engine to allow users to retrieve documents that contain(More)
Myeloproliferative neoplasms (MPNs) frequently have an activating mutation in the gene encoding Janus kinase 2 (JAK2). Thus, targeting the pathway mediated by JAK and its downstream substrate, signal transducer and activator of transcription (STAT), may yield clinical benefit for patients with MPNs containing the JAK2(V617F) mutation. Although JAK inhibitor(More)
Molecular targeted drugs are clinically effective anti-cancer therapies. However, tumours treated with single agents usually develop resistance. Here we use colorectal cancer (CRC) as a model to study how the acquisition of resistance to EGFR-targeted therapies can be restrained. Pathway-oriented genetic screens reveal that CRC cells escape from EGFR(More)
This paper introduces the novel research of emotion analysis from both the writer's and reader's perspectives. A challenge that comes up is the lack of a corpus annotated with both writer and reader emotions. We tackle this problem by combining an online writer-emotion corpus and an online reader-emotion corpus. Statistical analyses are then performed on(More)
This paper presents two approaches to ranking reader emotions of documents. Past studies assign a document to a single emotion category, so their methods cannot be applied directly to the emotion ranking problem. Furthermore, whereas previous research analyzes emotions from the writer’s perspective, this work examines readers’ emotional states. The first(More)
Capturing users' future search actions has many potential applications such as query recommendation, web page re-ranking, advertisement arrangement, and so on. This paper predicts users' future queries and URL clicks based on their current access behaviors and global users' query logs. We explore various features from queries and clicked URLs in the users'(More)