| Abstract | In this paper, we adopt two views, personal and impersonal views, and systematically employ them in both supervised and semi-supervised sentiment classification . |
| Abstract | On this basis, an ensemble method and a co—training algorithm are explored to employ the two views in supervised and semi-supervised sentiment classification respectively. |
| Introduction | As a special task of text classification, sentiment classification aims to classify a text according to the expressed sentimental polarities of opinions such as ‘thumb up’ or ‘thumb down’ on the movies (Pang et al., 2002). |
| Introduction | In general, the objective of sentiment classification can be represented as a kind of binary relation R, defined as an ordered triple (X, Y, G), where X is an object set including different kinds of people (e. g. writers, reviewers, or users), Y is another object set including the target objects (e.g. |
| Introduction | The concerned relation in sentiment classification is X ’s evaluation on Y, such as ‘thumb up’, ‘thumb down’, ‘favorable’, |
| Abstract | We conduct experiments in the field of cross-language sentiment classification , employing English as source language, and German, French, and Japanese as target languages. |
| Conclusion | The analysis covers performance and robustness issues in the context of cross-language sentiment classification with English as source language and German, French, and Japanese as target languages. |
| Cross-Language Structural Correspondence Learning | Considering our sentiment classification example, the word pair {excellent3, exzellentT} satisfies both conditions: (1) the words are strong indicators of positive sentiment, |
| Experiments | We evaluate CL—SCL for the task of cross-language sentiment classification using English as source language and German, French, and Japanese as target languages. |
| Experiments | We compiled a new dataset for cross-language sentiment classification by crawling product reviews from Amazon. |
| Experiments | Statistical machine translation technology offers a straightforward solution to the problem of cross-language text classification and has been used in a number of cross-language sentiment classification studies (Hiroshi et al., 2004; Bautin et al., 2008; Wan, 2009). |
| Introduction | 7 may be a spam filtering task, a topic categorization task, or a sentiment classification task. |
| Introduction | In this connection we compile extensive corpora in the languages English, German, French, and Japanese, and for different sentiment classification tasks. |
| Introduction | Section 5 presents experimental results in the context of cross-language sentiment classification . |