意見單元是評價對象及其對應意見詞的組合。意見單元的抽取是為情感分析領域的基礎任務之一。本研究提出了一個應用於中文部落格及論壇「智慧型手機」產品評論文章，基於語句層級中文語法規則的意見單元自動抽取方法。 本研究採用系統發展研究方法，建置一套雛型系統，此系統實作了建立意見單元抽取模式的流程。其中使用資料探勘分類技術進行訓練及測試，自動歸納出意見單元的抽取規則，以建立意見單元抽取模式。雛型系統以來自中文部落格及論壇，關於「智慧型手機」產品的評論文章做為來源。我們將本研究所建立的意見單元抽取模式的意見單元抽取結果與人工抽取結果相比較，並與相關研究方法比較，其中我們以F-Measure做為雛型系統主要評估的指標。 透過雛型系統的評估結果發現，在中文「智慧型手機」產品評論文章中，本研究建立的意見單元抽取模式，與相關研究使用字詞距離及比對句法路徑模式庫的意見單元抽取方法相比，在F-Measure皆有提升。另外，我們也發現同時使用語句結構與句法路徑結構作特徵屬性，有助於本系統意見單元抽取模式品質的提升，且語句結構在意見單元抽取較句法路徑結構具影響性。 本研究最後歸納在進行意見單元抽取時，能夠取得較佳結果的資料探勘分類技術與輸入特徵屬性類型的組合，作為實際運用時的建議。同時驗證本研究建置的建立意見單元抽取模式流程，對於意見單元的正確抽取是有幫助的。 Opinion unit is a combination of evaluation objects and corresponding opinion words. Opinion unit extraction is one of the basic tasks in the sentiment analysis field. This study proposes an opinion unit extraction method based on syntactic rules in Chinese. This study uses the systems development in information systems research to build a prototype system. We use classification techniques of data mining in the prototype system for training and testing to summarize opinion unit extraction rules and establish an opinion unit extraction mode. We use Chinese smartphone product review articles from the blog and forum to assess prototype system. We extract the comments regarding the mode of opinion unit recognition results and compared to artificial recognition results. Finally, we calculate the Precision, Recall and F-Measure e to validate the prototype system, and compare to related research methods. By evaluation of the prototype system and found that, we use our opinion unit extraction mode compared with the opinion unit extraction method based on word distance accuracy raised and compared with the opinion unit extraction method based on syntactic path accuracy raised in Chinese smartphone product review articles. In addition, we also found using the sentence structure and syntactic path structures as features will contribute to opinion unit extraction mode, and the statement structure is more influential in the opinion unit extraction. Finally, the study summarized the combination of the data mining classification techniques and characteristic attribute that can get the better result in extracting opinion unit as a recommendation for implementing. This study also confirms the establishment of the process of opinion unit extraction mode that it''s helpful for extracting opinion unit.
