憂鬱傾向者之微博書寫分析

• Main Authors: 任喆鸝, Ren, Zhe Li
• Language: 中文
• Published: 國立政治大學
• Subjects: 憂鬱人群; 書寫; 微博; 文本分析; Depressed crowd; Writing; Micro-blog; Text analysis
• Online Access: http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0101451029%22.

本文嘗試透過社群媒體微博進行憂鬱書寫識別，主要期望回答兩方面的問題：（一）中國憂鬱人群之社群媒體書寫特質為何？（二）如何透過該書寫特質識別更多的憂鬱文本？ 透過對十位已確認之憂鬱症患者之微博關係圈進行滾雪球，發現 127憂鬱傾向者，共爬取憂鬱傾向者之微博文本20748則，作為文本分析之數據集，並運用內容分析、質化分析、詞頻分析及詞語共現等多種方法分析文本。 分析結果顯示：(1)透過對文本進行語調、情緒、主題及憂鬱程度的編碼後，我們發現憂鬱傾向者在微博之書寫含62%的負面語調及25.1%的憂鬱文本，其中，負面及憂鬱程度較高的書寫主題是「自我」、「親情」、「自殺」及「睡眠障礙」。(2)深入對「自我」及「親情」憂鬱書寫的質化分析後，發現他們不同於一般人的心理特質，其中，「自我厭惡」及「不被理解」是他們心中最難以釋懷的角落。(3)由於「自殺」、「睡眠障礙」屬於憂鬱人群特徵，經過分析發現透過主題關聯詞的共現詞組有助於辨識憂鬱人群，其中，「睡眠障礙」共現詞的憂鬱文本辨識度達74%，「自殺」共現詞的憂鬱文本辨識度達34%，未來透過機器的方式，可進一步優化該方法，提升憂鬱文本的辨識度。 === This research aims to answer the following questions:(1)What are the characteristics of micro-blog writing by the depressed tendency people? (2)How to identify the text in social media? Ten Wei-bo users with identified depressed tendency were chosen as starting points of snow-ball searching, and 127 users were located. A total of 20748 messages from this group of the users was collected as the dataset. Multiple methods were applied to analyze the texts: content analysis, qualitative text analysis, word frequency analysis and word co-occurrence. The result indicated that: (1)Through the coding of the text tone, mood, theme and degree of depression, we find out that in micro blog writing, the depressive tendency uses 62% of the negative tone and 25.1% of the blue text. Among them, higher negative and degree of depression of writing subjects are "self", "family", "suicide" and "sleep disorder". (2)Through deep qualitative analysis of "self" and "affection" depressed writing, the "self loathing" and "don't understand" in their mind are the most unforgettable. (3)Because the depressed people have the features of "suicide" and "sleep disorder", through the analysis, we find that through theme related words, it is helpful in the identification of the depression text. Among them, the "sleep disorders" co-occurrence words depressed text identification is up to 74%, and "suicide" co-occurrence words depressed text identification degree is 34 %.In the future, through the computer, we can further optimize the method, and enhance the degree of identification of depression text.