政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111936
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    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/111936


    题名: Semantic frame-based approach for reade-remotion detection
    作者: Chang, Y.-C.
    Chen, Chen C.-C.
    Hsu, W.-L.
    贡献者: 資訊管理系
    关键词: Automation;Classification (of information);Information retrieval systems;Information systems;Learning systems;Semantics;Syntactics;Emotion detection;Frame-based;Semantic frames;Sentiment analysis;Text classification;Text processing
    日期: 2015-07
    上传时间: 2017-08-14 15:34:27 (UTC+8)
    摘要: Previous studies on emotion classification mainly focus on the writer`s emotional state. By contrast, this research emphasizes emotion detection from the readers` perspective. The classification of documents into reader-emotion categories can be applied in several ways, and one of the applications is to retain only the documents that trigger desired emotions to enable users to retrieve documents that contain relevant contents and at the same time instill proper emotions. However, current IR systems lack the ability to discern emotions within texts, and the detection of reader-emotion has yet to achieve a comparable performance. Moreover, previous machine learning-based approaches are generally not human understandable. Thereby, it is difficult to pinpoint the reason for recognition failures and understand the types of emotions articles inspire in their readers. In this paper, we propose a flexible semantic frame-based approach (FBA) for reader-emotion detection that simulates such process in a human perceptive manner. FBA is a highly automated process that incorporates various knowledge sources to learn semantic frames from raw text that characterize an emotion and are comprehensible for humans. Generated frames are adopted to predict reader-emotion through an alignment-based matching algorithm that allows a semantic frame to be partially matched through a statistical scoring scheme. Experimental results demonstrate that our approach can effectively detect reader-emotions by exploiting the syntactic structures and semantic associations in the context, while outperforming currently well-known statistical text classification method sand the stat-of-the-art reader-emotion detection method.
    關聯: Pacific Asia Conference on Information Systems, PACIS 2015 - Proceedings, (), -
    19th Pacific Asia Conference on Information Systems, PACIS 2015; Singapore; Singapore; 5 July 2015 到 9 July 2015; 代碼 125136
    数据类型: conference
    显示于类别:[資訊管理學系] 會議論文

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