TAILIEUCHUNG - Báo cáo khoa học: "A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge"

Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible to perform large-scale mining of public opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wisdom of crowds. In this paper, we consider the problem of learning high-quality sentiment models with minimal manual supervision. . | A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge Tao Li Yi Zhang School of Computer Science Florida International University taoli yzhan004 @ Vikas Sindhwani Mathematical Sciences IBM . Watson Research Center vsindhw@ Abstract Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs discussion forums and online review sites has made it possible to perform large-scale mining of public opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wisdom of crowds. In this paper we consider the problem of learning high-quality sentiment models with minimal manual supervision. We propose a novel approach to learn from lexical prior knowledge in the form of domain-independent sentimentladen terms in conjunction with domaindependent unlabeled data and a few labeled documents. Our model is based on a constrained non-negative tri-factorization of the term-document matrix which can be implemented using simple update rules. Extensive experimental studies demonstrate the effectiveness of our approach on a variety of real-world sentiment prediction tasks. 1 Introduction Web platforms such as blogs discussion forums and other such social media have now given a public voice to every consumer. Recent surveys have estimated that a massive number of internet users turn to such forums to collect recommendations for products and services guiding their own choices and decisions by the opinions that other consumers have publically expressed. Gleaning insights by monitoring and analyzing large amounts of such user-generated data is thus becoming a key competitive differentiator for many companies. While tracking brand perceptions in .

• Báo cáo khoa học
• A Non negative Matrix
• Tri factorization Approach to Sentiment Classification
• Lexical Prior Knowledge
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• BMC Bioinformatics
• Symmetric nonnegative matrix factorization
• Hessian regularization
• Multiview clustering
• Human microbiome
• Multiple biological networks
• Conserved modules
• Matrix factorization
• Multi layer network model
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo
• 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 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