Cross-Lingual Sentiment Quantification
نویسندگان
چکیده
منابع مشابه
Co-Training for Cross-Lingual Sentiment Classification
The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data. Machine tr...
متن کاملCross-Lingual Sentiment Analysis Without (Good) Translation
Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We a...
متن کاملActive Learning for Cross-Lingual Sentiment Classification
Cross-lingual sentiment classification aims to predict the sentiment orientation of a text in a language (named as the target language) with the help of the resources from another language (named as the source language). However, current cross-lingual performance is normally far away from satisfaction due to the huge difference in linguistic expression and social culture. In this paper, we sugg...
متن کاملCross-Lingual Mixture Model for Sentiment Classification
The amount of labeled sentiment data in English is much larger than that in other languages. Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e.g. Chinese) using labeled data in the source language (e.g. English). Most existing work relies on machine translation engines to directly adapt labele...
متن کاملCross-Lingual Sentiment Analysis with Machine Translation
Recent advancements in machine translation foster an interest of its use in sentiment analysis. This thesis investigates prospects and limitations of using machine translation in cross-lingual sentiment analysis. To perform a sentiment analysis we need to learn linguistic features by either using tools such as part-of-speech taggers, parsers, or basic resources such as annotated corpora or sent...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Intelligent Systems
سال: 2020
ISSN: 1541-1672,1941-1294
DOI: 10.1109/mis.2020.2979203