Unsupervised Sentiment Analysis of Objective Texts
| العنوان: | Unsupervised Sentiment Analysis of Objective Texts |
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| المؤلفون: | Qufei Chen, Marina Sokolova |
| المصدر: | Advances in Artificial Intelligence ISBN: 9783030183042 Canadian Conference on AI |
| بيانات النشر: | Springer International Publishing, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Computer science, business.industry, Sentiment analysis, computer.software_genre, Data set, ComputingMethodologies_PATTERNRECOGNITION, Empirical research, Benchmark (computing), Embedding, Unsupervised learning, Word2vec, Artificial intelligence, business, computer, Natural language processing, Word (computer architecture) |
| الوصف: | Unsupervised learning is an emerging approach in sentiment analysis. In this paper, we apply unsupervised word and document embedding algorithms, Word2Vec and Doc2Vec, to medical and scientific text. We use SentiWordNet as the benchmark measures. Our empirical study is done on the Obesity NLP Challenge data set and four Science subgroups from Reuters 20 Newsgroups. Our results show that Word2Vec demonstrates a reliable performance in sentiment analysis of the text, whereas Doc2Vec requires more detailed studies. |
| ردمك: | 978-3-030-18304-2 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::d38358a22cdf3f3eea9616d44a61de1f https://doi.org/10.1007/978-3-030-18305-9_45 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........d38358a22cdf3f3eea9616d44a61de1f |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783030183042 |
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