tailieunhanh - Báo cáo khoa học: " Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews"

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (., “subtle nuances”) and a negative semantic orientation when it has bad associations (., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between. | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 417-424. Thumbs Up or Thumbs Down Semantic Orientation Applied to Unsupervised Classification of Reviews Peter D. Turney Institute for Information Technology National Research Council of Canada Ottawa Ontario Canada K1A 0R6 Abstract This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended thumbs up or not recommended thumbs down . The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations . subtle nuances and a negative semantic orientation when it has bad associations . very cavalier . In this paper the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word excellent minus the mutual information between the given phrase and the word poor . A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74 when evaluated on 410 reviews from Epinions sampled from four different domains reviews of automobiles banks movies and travel destinations . The accuracy ranges from 84 for automobile reviews to 66 for movie reviews. 1 Introduction If you are considering a vacation in Akumal Mexico you might go to a search engine and enter the query Akumal travel review . However in this case Google1 reports about 5 000 matches. It would be useful to know what fraction of these matches recommend Akumal as a travel destination. With an algorithm for automatically classifying a review as thumbs up or thumbs down it would be possible for a search engine to report such summary statistics. This is the motivation for the research described here. Other potential applications include recognizing flames .

• Báo cáo khoa học
• Thumbs Up or Thumbs Down
• Semantic Orientation
• Unsupervised Classification of Reviews
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• THUMBS UP
• THUMBS DOWN
• BUILDER
• BOSS
• FRIENDSHIP
• FEELINGS BOARD
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học