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    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/111936


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111936


    Title: Semantic frame-based approach for reade-remotion detection
    Authors: Chang, Y.-C.
    Chen, Chen C.-C.
    Hsu, W.-L.
    Contributors: 資訊管理系
    Keywords: 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
    Date: 2015-07
    Issue Date: 2017-08-14 15:34:27 (UTC+8)
    Abstract: 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.
    Relation: 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
    Data Type: conference
    Appears in Collections:[資訊管理學系] 會議論文

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