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https://nccur.lib.nccu.edu.tw/handle/140.119/153392
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| Title: | Facebook上的情緒分析：台灣COVID-19期間疫苗貼文的研究
Sentiment Analysis on Facebook: A Case Study of Vaccine Posts during the COVID-19 in Taiwan |
| Authors: | 周廷威
Jhou, Ting-Wei |
| Contributors: | 邱淑怡
Chiu, Shu-I
周廷威
Jhou, Ting-Wei |
| Keywords: | COVID-19
社群媒體
疫苗
情緒分析
機器學習
COVID-19
Social media
Vaccine
Sentiment analysis
Machine learning |
| Date: | 2024 |
| Issue Date: | 2024-09-04 15:02:23 (UTC+8) |
| Abstract: | 本研究主要探討社群媒體之疫苗貼文的情緒分析，在疫情期間透過社群媒體接收疫情資訊日漸頻繁，快速獲得疫情新資訊的同時，也十分容易受貼文內容影響情緒，在許多人因疫情導致憂鬱、恐慌和害怕的時候，為了避免他們在收集解決狀況的資訊時心靈再次受到傷害，應該事先讓用戶可以知道他們瀏覽的貼文可能會帶給他們什麼樣的情緒。
本文將文本情感分為貼文作者和貼文讀者兩個方面的情感來進行分析：
作者方面視作貼文自身的情感，用雙向長短期記憶模型來學習社群媒體貼文的情感極性，BERT提取文本特徵後，透過Bi-LSTM模型進行訓練，隱藏層包括自注意力層、循環層和全連接層，模型結果呈現正向文本的預測非常的好，中立和負向的文本預測則是普通，會有預測錯誤的情形。
讀者方面使用Facebook reactions來計算對文本的好感程度，Facebook reactions本身即帶有情感定義，相關研究中也有被使用來分析讀者對貼本類別的喜好，但是疫情期間Facebook reactions新增了一個情感，因此計算正向和負向的公式已不適用，國外與台灣對於Facebook的使用習慣也有不同，因此要使用這個資料來進行情感分析需要重新修改情感定義和計算公式，調整過的公式在實驗得到的情感指數與疫情期間台灣對各廠牌疫苗的看法相似。
This study explores emotional analysis of vaccine-related social media posts during the pandemic. Excessive exposure to epidemic information on social media has made individuals susceptible to emotional influence. Amid feelings of depression, panic, and fear, it's vital to caution users about potential emotional impacts before viewing posts to prevent further distress while seeking solutions.
The study assesses emotions from post authors' and readers' perspectives. A Bi-LSTM model with BERT for text feature extraction is used to determine emotional polarity.
For reader analysis, Facebook reactions gauge post favorability. However, the pandemic's addition of new emotions in reactions challenges existing sentiment calculation formulas. Adjusted formulas in experiments resulted in sentiment indices akin to Taiwan's perceptions of vaccine brands during the pandemic. |
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| Description: | 碩士
國立政治大學
資訊科學系
111753223 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111753223 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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