本論文主要貢獻為提出美妝保養專有領域意見詞、比較不同意見辭典之搭配的效果，以及證實雙語意見傾向之評估具有輔助機器學習的效果。 Opinion Analysis is a task that aims to determine the subjective orientation in contexts of expressing opinions on the Internet using computational techniques of Natural Language Processing. Posting opinions on the Internet that use bilingual expression is an occasional case in Chinese reviews. However, very little attention has been given to bilingual expression of opinion analysis in prior research.
This paper proposes an approach, which focuses on bilingual opinion analysis applying multi-dictionary, machine learning and feature selection in the contexts of bilingual opinion in Chinese reviews.
We found that accuracy would be strongly affected by different sets of general sentiment dictionaries. Our optimal experiment results showed that the overall performance by using 21 features of our proposed system achieved 74.98% with accuracy of cross validation and 77.10% with accuracy of open test. In addition to the experimental results, we also discovered the influential trend of our system by the variation of proportion of English data in Chinese reviews.
The contributions of this paper are threefold: (1) extracting a new Chinese sentiment dictionary in the field of cosmetic reviews from our experiment, (2) comparing the influences in different sentiment dictionaries, and (3) proving that bilingual opinion analysis can facilitate the performance of machine learning in Chinese reviews.
