在網際網路的口碑與評論中，評價詞彙會隨著領域變化，因為人們會用不同的評價語句來表達自己的意見，所以特定領域的話題所使用的詞彙是很重要的，在不同領域中的情緒詞彙可能極為相似。然而在網際網路資訊成長的同時，許多不同的特定領域所使用屬性詞彙、評價詞彙也隨之大量增加，並且被廣泛的使用，傳統的評價詞庫已逐漸不敷使用。本研究所建立之雛型系統以及分類模型，了解文章領域分類效果之影響以及對目標領域意見單元萃取效果之影響，以萃取出與目標領域相關的意見單元組合。本研究提出一套雛型系統以及領域詞庫選擇分類模型，實驗中發現對於領域詞庫選擇的預測有著明顯的影響，交叉驗證準確度可達83.35%，而開放測試準確度達到84.8%，領域正面意見單元擷取提升24.2%，領域負面意見單元擷取提升22.9%。 Blooming Internet social media produces huge people opinions and comments. Hence, to analyze those text contents is necessary to have much more complicated with domain oriented sentiment wordings. However, categorizing specific-domain meanings of sentiment wordings and to help for building significant domain dictionary is important for rising accuracy rate of extraction and evaluation opinion units from text contents. In this paper, we propose prototype system and the classification model to describe the text dependency with domain classification and the efficiency of opinion unit extraction form specific target domain. To prove this domain responsive dictionary classified system prototype, the experiment results showed that the overall performance of our proposed system achieved 83.35% with accuracy of cross validation and 84.8% with accuracy of open laboratory test. Furthermore, this system validation is found on fetching correct positive opinion units rising to 24.2% as well as retrieving correct negative opinion unit increasing to 22.9% with domain responsive dictionary.
