產品行銷廣告文之分類與特徵分析

Feature extraction and classification of product advertising review

劉羽欣(Yu-Hsin Liu)

指導教授 ： 王正豪

國立臺北科技大學/電資學院/資訊工程系研究所/碩士(2013年)

摘要

伴隨著網路蓬勃發展，網路早已成為各大廠商所注重的行銷通路，因此開始有廠商贈予商品或是稿費請部落客或民眾在網路上撰寫產品使用文章以推銷該品牌之產品。此類文章數量越來越龐大，但是文章的真實性難以驗證，是否產品確實如廣告文內容描述之效果，或是因為筆者為獲取稿費而對產品的評論帶有偏頗，以傳統文件內容分類的方法可能無法有效評斷。
因此，本論文提出一個產品廣告文特徵擷取方法及分類模型，透過文章中正面情緒詞比例、總字數、圖片數量、稱讚詞比例、發表日期等特徵以訓練SVM分類模型。實驗中針對網誌美妝產品評論文章2150篇進行分類，結果對於一般文章與美妝廣告文章的分類效果F-measure為94%，與傳統的文件分類方法TF-IDF不相上下，效率卻高了許多。對於美妝非廣告文與美妝廣告文之分類本論文提出之方法亦可達一良好準確度，顯示了此方法的實用性。

關鍵字

文件分類 ； 廣告文 ； 特徵擷取

並列摘要

Web has become an important place for marketing in business. Many vendors offer bloggers or people their products or payment and ask them to write review of product using experience to promote their products. However, it’s hard to identify the truthfulness of these reviews. By using conventional text classification methods by content, it is difficult to distinguish between real and fake reviews.
In this paper, we propose a feature extraction method and classification model for advertising reviews. Based on features like ratio of positive opinion terms, number of pictures, ratio of praiseful words, and publishes date; we train a SVM classifier for advertising review identification. In our experiment, we collected 2150 reviews in the “cosmetics” domain. For classifying advertising reviews in cosmetics domain and other articles, our method can perform 94% at F-measure. This result is comparable to the conventional approach of document classification using TF-IDF, and our method is more efficient in training. For classifying advertising and ordinary non-advertising reviews in cosmetics domain, our method also can achieve good classification accuracy. It shows the feasibility of practical use in advertising reviews classification.

並列關鍵字

Document Classification ； Advertising reviews ； feature extraction

參考文獻

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