and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain – movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis

The analysis of the relationship between movie reviews and consumer’s decision process has focused mainly on the side of critics, who have been defined as “influencers ” or as “predictors ” (Eliashberg & Shugan, 1997). Also, new ways to measure the impact of the critic have been introduced

aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain – movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis

The goal of sentiment classification is to determine whether or not an author likes what he is writing about. In this paper, we explore various ways to improve upon current techniques involving subjectivity filters and information gain based feature selection. We conclude that the subjectivity filter is useful in certain cases, but needs to be explored further. 1

This paper presents an empirical study of efficacy of machine learning techniques in classifying text messages by semantic meaning. We use movie review comments from popular social network Digg as our data set and classify text by subjectivity/objectivity and negative/positive attitude. We propose

Abstract: Sentiment analysis is the mining the sentiment or opinion words and identification and analysis of the opinion and arguments in the text. Text document clustering contains opinions or sentiments about the objects, such as product reviews, movie reviews, and book reviews etc. This paper

We consider the problem of predicting a movie’s opening weekend revenue. Previous work on this problem has used metadata about a movie—e.g., its genre, MPAA rating, and cast—with very limited work making use of text about the movie. In this paper, we use the text of film critics ’ reviews from

With the rapidly increasing amount of text available on the internet, organizing this vast amount of information has become increasingly important. Many researchers in natural language processing have studied the problem of automatically assigning documents to different categories. One type of categorization that has been studied is classifying the sentiment found in documents. Although many

, publications do not give especially generous reviews to films distributed by their corporate parents, suggesting that ownership may not be a major source of valence bias for particular corporate interests, nor perhaps, for corporate class interests either. The critical tradition of mass communication

Web content mining is intended to help people discover valuable information from large amount of unstructured data on the web. Movie review mining classifies movie reviews into two polarities: positive and negative. As a type of sentiment-based classification, movie review mining is different from