基於長短期記憶模型之違規廣告及違規詞識別技術

Recognition Technology of Illegal Advertisements and Illegal Words Based on Long Short-Term Memory Model

呂景翔(Ching-Hsiang Lu)

指導教授 ： 張志勇

共同指導教授 ： 石貴平

淡江大學/工學院/資訊工程學系碩士在職專班/碩士(2021年)

https://doi.org/10.6846/TKU.2021.00748

摘要

網路時代的來臨，也意味著傳統廣告行銷的大遷徙，廣告的投放開始轉向可以帶來更大流量的社群媒體如:Google、Facebook、Youtube。然而，隨著行銷環境的改變，社群媒體的即時性及高自由度也帶來了許多問題，違規廣告案件層出不窮，其中尤以食品、藥物及化妝品廣告為大宗，同時食品藥物管理署對於涉及誇張或易生誤解的詞句之標準認定不易區分，如涉及誇張、易生誤解或醫療效能的「增強抵抗力」、「強化細胞功能」、「解酒」等，與食品藥物管理署認定「可使用」詞句像是「幫助牙齒骨骼正常發育」、「幫助消化」、「改變細菌叢生態」、「使排便順暢」及「調整體質」、「青春美麗」等，訂定的標準不易判斷是否違規，消費者較難辨別這樣的宣稱是不是誇大不實，也因此造成食品醫療廣告違規數居高不下。
大部分違規事項是刻意針對療效進行誇大不實的宣傳，意圖誘使消費者購買，綜上所述歸納出幾點問題如下 :
1. 針對食品醫藥廣告違規，目前我國法令採事後裁罰，由執法人員人工審查認定，難以即時發現違規廣告並且效率過低。
2. 食品醫藥廣告用語繁多，以人工審查核定難免過於主觀，造成業者難以預料廣告是否違規。
3. 各縣市都有發布違規廣告相關資訊的平台，但是沒有整合違規廣告詞的地方，方便使用者確認是否違規
本研究將針對這三點問題設計食品醫藥違規廣告偵測流程與方法，並進行一系列實驗驗證其流程與方法的正確性。

關鍵字

合法字詞識別 ； 違法字詞識別 ； LSTM

並列摘要

The advent of the Internet era causes a great migration of traditional advertising marketing. The advertisement has been shift to social media such as Google, Facebook, and Youtube that can bring more traffic. However, with the changes in the marketing environment, the immediacy and high degree of freedom of social media have also brought many problems, and cases of illegal advertising have emerged one after another, especially food, drug and cosmetics advertising. To identify the exaggerated or misunderstood words and sentences are not easy, such as "enhancing resistance", "strengthening cell function", "relieving alcohol", etc., which involve exaggeration, misunderstanding, or medical efficacy. The phrases such as "help the normal development of teeth and bones", "help digestion", "change the ecology of the bacterial flora", "make defecation smoother" and "adjust physical fitness", "beauty and youth", etc are also difficult to identify whether or not the advertising are illegal.
This thesis aims to investigate the following issues:
1. Regarding food and medicine advertising violations, the current Chinese laws and regulations adopt post-mortem penalties, which are manually reviewed and determined by law enforcement personnel. It is difficult to find illegal advertisements immediately and the efficiency is too low.
2. There are so many words in food and medicine advertisements, and manual review and approval is inevitably too subjective, making it difficult for the industry to predict whether the advertisement is in violation of regulations.
3. Each county and city has a platform for publishing information about illegal advertisements, but there is no place to integrate illegal advertisements to facilitate users to confirm whether they are in violation
This research will design the food and medicine illegal advertising detection process and method for the three issues, and conduct a series of experiments to verify the correctness of the process and method.

並列關鍵字

Legal word recognition ； Illegal Word recognition ； LSTM

參考文獻

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[4] Khan, J. Y., Khondaker, M., Islam, T., Iqbal, A., & Afroz, S. (2019). A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749.

[5] Zhao, F., Skums, P., Zelikovsky, A., Sevigny, E. L., Swahn, M. H., Strasser, S. M., & Wu, Y. (2019, June). Detecting Illicit Drug Ads in Google+ Using Machine Learning. In International Symposium on Bioinformatics Research and Applications (pp. 171-179). Springer, Cham.