本篇論文致力於研究評論文章的評價等級類別，提出一個平價分類系統能自動分類評論文章其所相對應的評價等級類別。本研究係利用擷取出訓練文章中的各種特徵，並使用機器學習訓練模組求得各個特徵與等級類別之間的相關性，進而訓練出一套評價分類系統。
我們採用最大熵值法(Maximum Entropy, ME)作為我們的機器訓練模組，在訓練過程中，我們利用網路上所收集而來的評論相關文章與一部分類辭典，擷取出具有意見的詞彙、片語等特徵，並將這些特徵集送入ME作訓練，最後求得一套評價分類模組。在執行階段，輸入一篇評論文章，利用上述特徵擷取方式求得特徵集，最後利用訓練而得的分類模組輸出相對應的評價等級。

This paper concentrates on the study of opinion classification task. We propose a method to automatically class review with appropriate evaluation category. Our method utilizes extracting features from the training data and uses machine learning algorithm to train an evaluation classification system.We select Maximum Entropy (ME) as our machine learning module. In training time, we utilized the review corpus from the Web and a category dictionary to extract opinion words and phrases as our feature set. And then we used ME to train with feature set to get an evaluation classification module. At run time, a given review is automatically transformed into a feature set and sent to a classification module, and then return a suitable evaluation.

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