| Abstract | This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet. |
| Abstract | The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and small—set in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually. |
| Domain Adaptation in Sentiment Research | There are two alternatives to supervised machine learning that can be used to get around this problem: on the one hand, general lists of sentiment clues/features can be acquired from domain-independent sources such as dictionaries or the Internet, on the other hand, unsupervised and weakly-supervised approaches can be used to take advantage of a small number of annotated in-domain examples and/or of unlabelled in-domain data. |
| Domain Adaptation in Sentiment Research | But such general word lists were shown to perform worse than statistical models built on sufficiently large in-domain training sets of movie reviews (Pang et al., 2002). |
| Domain Adaptation in Sentiment Research | For instance, Aue and Gamon (2005) proposed training on a samll number of labeled examples and large quantities of unlabelled in-domain data. |
| Introduction | Many applications require reliable processing of heterogeneous corpora, such as the World Wide Web, where the diversity of genres and domains present in the Internet limits the feasibility of in-domain training. |
| Introduction | A number of methods has been proposed in order to overcome this system portability limitation by using out-of-domain data, unlabelled in-domain corpora or a combination of in-domain and out-of-domain examples (Aue and Gamon, 2005; Bai et al., 2005; Drezde et al., 2007; Tan et al., 2007). |
| Introduction | The information contained in lexicographical sources, such as WordNet, reflects a lay person’s general knowledge about the world, while domain-specific knowledge can be acquired through classifier training on a small set of in-domain data. |