隨著網路技術的快速發展，大部份消費者在購買某項產品或某公司的服務時，會先瀏覽網站上的相關評論之後，再進行購買行為。相關研究指出，評論文章會影響消費者對產品或某公司服務的購買決策。所以對於公司而言，公司利用顧客評論的文章做各面向(aspect)的口碑分析及查看網友們的意見，以便於能在最短的時間內回覆和平衡與公司相關的負面評價，這是件非常重要的工作。因此就台灣的公司必須利用中文意見探勘系統來做各面向的口碑分析，我們已經初步發展了一個屬於aspect-level的中文意見探勘系統；此系統分別利用default topic和 default feature來增加意見和所要討論面向的回收率。此研究我們將利用意見元素間的上下文關聯性讓系統不但能推論出部分網友在回文中所要討論的topic、面向和子面向，同時亦能修正部分由default topic和 default feature所造成的錯誤；此增加的功能是能讓使用者在做口碑分析時，能獲得更詳細和正確的資訊。 With the development of network technology, before people buy a product or a company''s service, most consumers will search the related comments on the social networking sites. Related studies indicate that the product evaluation article will influence the purchase decisions of consumers. Therefore, analyzing WOM (word of mouth) to different aspects and replying negative WOM are important things to companies. Since opinion analysis at document level and sentence level is too coarse to determine users’ opinions precisely, we have developed an aspect-level Chinese opinion mining system for a specific domain. The system uses default topic and default feature to increase recall rate of opinion mining results and aspects, respectively. In this project, we will use context dependent not only to derive some unknown topics, features and sub-features but also to correct some default topic and default feature errors. Consequently, uses can get more detail and correct information from WOM analysis.
