TAILIEUCHUNG - Báo cáo khoa học: "Automatic Identification of Pro and Con Reasons in Online Reviews"

In this paper, we present a system that automatically extracts the pros and cons from online reviews. Although many approaches have been developed for extracting opinions from text, our focus here is on extracting the reasons of the opinions, which may themselves be in the form of either fact or opinion. Leveraging online review sites with author-generated pros and cons, we propose a system for aligning the pros and cons to their sentences in review texts. | Automatic Identification of Pro and Con Reasons in Online Reviews Soo-Min Kim and Eduard Hovy USC Information Sciences Institute 4676 Admiralty Way Marina del Rey CA 90292-6695 skim hovy @ Abstract In this paper we present a system that automatically extracts the pros and cons from online reviews. Although many approaches have been developed for extracting opinions from text our focus here is on extracting the reasons of the opinions which may themselves be in the form of either fact or opinion. Leveraging online review sites with author-generated pros and cons we propose a system for aligning the pros and cons to their sentences in review texts. A maximum entropy model is then trained on the resulting labeled set to subsequently extract pros and cons from online review sites that do not explicitly provide them. Our experimental results show that our resulting system identifies pros and cons with 66 precision and 76 recall. 1 Introduction Many opinions are being expressed on the Web in such settings as product reviews personal blogs and news group message boards. People increasingly participate to express their opinions online. This trend has raised many interesting and challenging research topics such as subjectivity detection semantic orientation classification and review classification. Subjectivity detection is the task of identifying subjective words expressions and sentences. Wiebe et al. 1999 Hatzivassiloglou and Wiebe 2000 Riloff et al 2003 . Identifying subjectivity helps separate opinions from fact which may be useful in question answering summarization etc. Semantic orientation classification is a task of determining positive or negative sentiment of words Hatzivassiloglou and McKeown 1997 Turney 2002 Esuli and Sebastiani 2005 . Sentiment of phrases and sentences has also been studied in Kim and Hovy 2004 Wilson et al. 2005 . Document level sentiment classification is mostly applied to reviews where systems assign a positive or negative sentiment

• Báo cáo khoa học
• Automatic Identification of Pro
• Con Reasons in Online Reviews
• Soo Min Kim and Eduard Hovy
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• 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