基於照片共享社群媒體的文章圖片和內文分類使用者性別：以Instagram為例

Gender classification on photo-sharing social media based on images and content analysis: a case study of Instagram

王怡蓁(Yi-Chen Wang)

指導教授 ： 吳家齊

淡江大學/商管學院/管理科學學系企業經營碩士班/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00390

摘要

近年來，社群媒體日漸興起，若是能從中有效的掌握使用者特徵，企業在行銷及推廣上，便能更迅速的掌握目標客群。本研究欲針對近幾年用戶參與度逐年倍增，以照片分享為宗旨的社群媒體Instagram平台作為分析對象，但在以往的研究中，大多只對發布的內文、文字部分作探討，在此種照片共享社群平台上無法有效分析其使用者特徵。
透過搜集Instagram公開使用者去推論其男女性別。最終得到的結果，在文字部分獲得了76.32%的推論性別準確率，反觀利用圖片分析去推論使用者性別，準確率高達92.11%。且若是將使用者資訊、圖片和文字的三部分的屬性皆匯入類神經網路分類器中，其得出的準確率為89.47%。
本研究發現，在照片共享社群平台上，光是透過圖片分析去建構分類模型，就能夠比文字或是其他結構化資訊，更有效的去判斷使用者男女性別特徵。

關鍵字

使用者特徵 ； 機器學習 ； 文字探勘 ； 圖片分類 ； 社群媒體探勘

並列摘要

In recent years, with the rise of social media, companies can effectively grasp target customers in marketing and promotion if they can effectively grasp user profiles. This study aims to analyze social media platform that are mainly based on photo-sharing. However, most of the previous studies only discussed the content and text of posts to analyze user profiles.
In this study, we collected public users of Instagram to classify their gender. In the final result, only 73.68% of the inferred gender accuracy rate was obtained in the text part. In contrast, by using image analysis to classify the gender of users, the accuracy rate is as high as 92.11%. In addition, if the attributes of user information, images, and text are integrated into the neural network classifier, the accuracy rate is 89.47%.
Through this study, we found out that the classification model constructed only by image analysis is more effective than text or other structured information to infer the gender characteristics of users on photo-sharing social media.

並列關鍵字

User Profile ； Machine Learning ； Text Mining ； Picture classification ； Social Media Mining

參考文獻

網路資源

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英文文獻