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Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/2940

Title: 混合語意主角及統計式K-最鄰近點方法於情緒詞擴充部落格心情文章量化追蹤
Department of Information Management , Shu-Te University Hybrid semantic roles and KNN approach to emotion word expansion and quantization for monitoring blogs
Authors: 崔博喻
Po-Yu Tsui
Contributors: 資訊管理系碩士班
陳璽煌
Keywords: 網際網路;部落格;RSS;自然語言;K-最鄰近分類法;語意主角
Internet;Blog;RSS;Natural language;K-Nearest Neighbor;Semantic Roles
Date: 2012
Issue Date: 2012-11-13 17:01:06 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理系碩士班]
Abstract: 校園以往被認定是較單純環境,也是較自主、較具學術自由的領域。隨著社會環境的變遷,學生成長過程正接受著課業、身體改變、人際關係、對抗外在誘惑與父母分離獨立自主的壓力等等,常使學生徘徊於傍徨無助中,美國大學健康協會於2006年全國大學健康評鑑顯示,有94%學生曾被要求要做的事情承受巨大壓力,44%學生表示曾經感到非常沮喪而難以正常生活,1.3%學生表示曾經想自殺。由本國教育部每年各學級學校校園事件統計分析報告顯示,通報件數履創新高,以98學年度分析為例學生自殺、自傷案件,大專校院107件、高中職132件、國中78件、國小16件,另外校園兒少保護、暴力偏差與管教衝突共有8792件,造成整體社會很大的負擔。
近幾年校園與家庭網際網路普遍化,許多學生擁有自己的部落格、臉書等等,並且不定時在相關平台發表心情日誌,然而這些文章撰寫學生情緒與對於事件的看法,有鑑於此,本研究擬設計及發展一套憂鬱情緒量化評估系統,透過RSS可訂閱部落格文章特性收集學生撰寫的文章,運用自然語言相關技術,從撰寫文字中預估其憂鬱程度,並由曲折線圖顯示近期每篇文章憂鬱程度,提供心理諮商師做為心理輔導的參考,達到降低憂鬱傾向學生意外發生率。本系統共包含建立學生部落格心情文章資料庫、情緒詞憂鬱度評分擴充系統、文章憂鬱度量化系統。在學生部落格心情文章資料庫中,學生可自行在部落格撰寫文章,系統將定時透過RSS至部落格收集學生所撰寫文章,經由文章憂鬱度量化系統進行自然語言剖析取得每段語句中含有的情緒詞與對應的語意主角,並運用K-最鄰近點分類法建立情緒詞憂鬱度評分擴充系統,以情緒詞憂鬱度分數與語意主角分析建立憂鬱情緒量化評估系統。
本研究結果顯示,原本參考詞庫憂鬱度分數為同一群組取最近中心點情緒詞憂鬱度分數為整個群組情緒詞憂鬱度分數,經K-最鄰近點分類法擴充的情緒詞庫重新各別配給情緒詞新的憂鬱度分數,且有效提升正向情緒詞中含有負向情緒詞的憂鬱度分數,而經由語意主角分析也排除非撰寫者情緒感知詞彙,提供更為精確的憂鬱度量化分數,心理諮商師可借由系統憂鬱度量化曲線圖監控學生近期情緒的波動起伏,有效進行相關輔導作業達到預防學生意外傷害案件的發生。
Campus was previously regarded as a simpler environment. It was also a territory with relatively more autonomy and more academic freedom. However, with the change of social environment, a student's growing process is now suffering pressures from academic requirements, physical changes, inter-personal relationship, external temptation and independence from parents. These pressures often leave students into lingering in the midst of helplessness. A national college health evaluation conducted by the American College Health Association in 2006 indicated that 94% of students suffered from tremendous pressure because of things they were requested to do, 44% of students said they once felt very depressed and hard to lead a normal life and 1.3% of students said they thought about killing themselves. According to the annual campus incident statistics for various grades of schools conducted by the National Ministry of Education each year, the number of incidents reported hits record high each year. Take case analysis for student suicide and self-inflicted injuries in 2009 school year for example, there were 107 such cases for college, 132 for senior high school, 78 for junior high school and 16 for primary school. Additionally, there were a total of 8,792 reported cases of child abuse, violent deviance behavior and discipline conflict on campus. This has created a cumbersome burden to the society as a whole.
With the popularization of internet on campus and at home in recent years, many students have their own blogs and facebook accounts. They will post their diaries documenting their mood on related platforms from time to time. Some of these articles posted described these students' mood and their views on certain incidents. Accordingly, this study tries to design and develop a system to quantify and assess depressed mood. Through RSS, we're able to subscribe characteristics of these articles posted on blogs and collect articles written by students. With the utilization of related techniques on natural language, we can estimate the degree of depression from these articles and point out, through trend lines, recent degree of depression on each article. These will serve as counseling psychologist's references during counseling in order to reduce accident rates for students with the tendency of depression. This system includes setting up of a database for students' articles depicting their mood on blogs, assessment expansion system for degrees of depressed mood and a quantify system on the depression degrees of articles. Students are able to write articles on their blogs and the system will regularly collect, through RSS, their articles on the blogs. Natural language analysis will be conducted using article depression quantifying system. With this, emotional terms and their responding linguistic characters on each paragraph will be obtained. An assessment expansion system for degrees of depressed mood will then be established using K-Nearest Neighbor Classifier method. Finally, a depression mood degree quantify system will be built up based on depression mood degrees and analysis on linguistic characters.
According to the result of this study, for the group originally with the same reference terminology database depression degree, the emotional terminology depression degree closest to the center point was designated as the emotion terminology depression degree for the whole group. However, after the expansion on K-Nearest Neighbor Classifier method, the emotional terminology database re-designated new depression degrees onto respective emotional terminologies and effectively rectified negative emotional terminology depression degrees embedded in positive emotional terminologies. In the meantime, through analysis over Semantic Roles, emotion detection terminologies from non-writers were excluded accordingly. This has provided more accurate depression quantified degrees. Counseling psychologists will be able to monitor students' recent emotional ups and downs using systematic depression quantifying trend line charts. This will ensure effective processing of related counseling operation and prevent the occurrence of student accidents and injuries.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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