Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment analysis systems. Previously, sentiment analysis was mostly studied under data-driven and lexicon-based frameworks. Such work generally exploits textual features for fact-based analysis tasks or lexical indicators from a sentiment lexicon. We propose to model term weighting into a sentiment analysis system utilizing collection statistics, contextual and topicrelated characteristics as well as opinionrelated properties. Experiments carried out on various datasets show that our approach effectively improves previous methods. . | Discovering the Discriminative Views Measuring Term Weights for Sentiment Analysis Jungi Kim Jin-Ji Li and Jong-Hyeok Lee Division of Electrical and Computer Engineering Pohang University of Science and Technology Pohang Republic of Korea yangpa ljj jhlee @postech.ac.kr Abstract This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment analysis systems. Previously sentiment analysis was mostly studied under data-driven and lexicon-based frameworks. Such work generally exploits textual features for fact-based analysis tasks or lexical indicators from a sentiment lexicon. We propose to model term weighting into a sentiment analysis system utilizing collection statistics contextual and topic-related characteristics as well as opinion-related properties. Experiments carried out on various datasets show that our approach effectively improves previous methods. 1 Introduction With the explosion in the amount of commentaries on current issues and personal views expressed in weblogs on the Internet the field of studying how to analyze such remarks and sentiments has been increasing as well. The field of opinion mining and sentiment analysis involves extracting opinionated pieces of text determining the polarities and strengths and extracting holders and targets of the opinions. Much research has focused on creating testbeds for sentiment analysis tasks. Most notable and widely used are Multi-Perspective Question Answering MPQA and Movie-review datasets. MPQA is a collection of newspaper articles annotated with opinions and private states at the subsentence level Wiebe et al. 2003 . Movie-review dataset consists of positive and negative reviews from the Internet Movie Database IMDb archive Pang et al. 2002 . Evaluation workshops such as TREC and NT-CIR have recently joined in this new trend of research and organized a number of successful meetings. At the TREC Blog
