We study the classification of news articles into emotions they invoke in their readers. Our work differs from previous studies, which focused on the classification of documents into their authors' emotions instead of the readers'. We use various combinations of feature sets to find the best combination for identifying the emotional influences of news articles on readers.
An emotion lexicon is an indispensable resource for emotion analysis. This paper aims to mine the relationships between words and emotions using weblog corpora. A collocation model is proposed to learn emotion lexicons from weblog articles. Emotion classification at sentence level is experimented by using the mined lexicons to demonstrate their usefulness.
This paper investigates three multilingual named entity corpora, including named people, named locations and named organizations. Frequency-based approaches with and without dictionary are proposed to extract formulation rules of named entities for individual languages, and transformation rules for mapping among languages. We consider the issues of abbreviation and compound keyword at a distance. Keywords specify not only the types of named entities, but also tell out which parts of a named entity should be meaning-translated and which part should be phoneme-transliterated. An application of the results on cross language information retrieval is also shown.
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