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作者(中文):黃郁茹
作者(外文):Huang, Yu-Ju
論文名稱(中文):隱藏式情緒語意模式分析
論文名稱(外文):Finding the Emotion Patterns without Explicit Emotion Words.
指導教授(中文):陳宜欣
指導教授(外文):Chen, Yi-Shin
口試委員(中文):楊奕軒
張仁和
口試委員(外文):Yang, Yi-Hsuan
Chang, Jen-Ho
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學號:103065507
出版年(民國):106
畢業學年度:105
語文別:英文
論文頁數:33
中文關鍵詞:情緒語意模式文字探勘
外文關鍵詞:emotion patterntext mining
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使用者經由發佈社群網路文章,來紓發日常的心得與觀察,已是再平常不過的事。藉由文章當中語意模式的截取方法,可以找出文章裡所表露的情緒,在本研究中進一步探索隱藏性情緒語意模式(亦即不帶有字典中定義的顯性情緒字的語意模式)在不同的情緒上的具有何特質,其情緒分數與模式之間的關聯如何。透過語意模式清單的建置,針對快樂、生氣、憂傷、恐懼、希望以及驚喜這六種情緒,探索在各別的情緒上,這些不帶有明顯情緒字的語意模式,具體是如何,而當中又有多少與特定的情緒有高度相關。研究結果顯示不帶有情緒字的隱藏性情緒語意模式可以幫助判別訊息中的單一情緒。
With the widespread usage of social media, the amount data that these platforms generate has made it possible to analyze the emotions and opinions within text messages. Most of the existing research related to emotion classification utilize a set of agreed-upon emotion specific words, and ignore the presence of stop words; which were supposed to bear no emotional meaning when used on their own. In this research we present our work on Implicit Emotion Patterns, which are composed only of stop words. The extracted patterns are highly relevant to a specific emotion in the set of joy, anger, sadness, surprise, hope, or fear; based on their emotion score. We set thresholds on the emotion score for each emotion to filter out these patterns. To evaluate, we prepare an experiment with a sample of tweets containing these Implicit Emotion Patterns for each of the emotions, and examine their relationship with the emotions outline. We asked annotators to check the agreement of the emotions they perceive in the tweets. We find that these Implicit Emotion Patterns are highly related to the emotions they were relevant to. In this work, we contribute to the domain a dictionary of stop words that display a relationship with emotions and outline our method for examining this hypothesis.
摘要

i

Abstract

i

Acknowledgement

ii

List of Tables

v

List of Figures

vi

1 Introduction

1

2 Related Work

3

3 Methodology

8

3.1

Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2

Emotion Word . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3

Pattern Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.4

emotion specific pattern Selection . . . . . . . . . . . . . . . . . . . . . . 13

3.5

Data Collecting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.6

Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Experiment & Results

16

iii

5 Discussion

26

6 Conclusion

30

References

31

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