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EnglishWord embeddings or distributed representations of words are being used in various applications like machine translation, sentiment analysis, topic identification etc. Quality of word embeddings and performance of their applications depends on several factors like training method, corpus size and relevance etc. In this study we compare performance of a dozen of pretrained word embedding models on lyrics sentiment analysis and movie review polarity tasks. According to our results, Twitter Tweets is the best on lyrics sentiment analysis, whereas Google News and Common Crawl are the top performers on movie polarity analysis. Glove trained models slightly outrun those trained with Skipgram. Also, factors like topic relevance and size of corpus significantly impact the quality of the models. When medium or large-sized text sets are available, obtaining word embeddings from same training dataset is usually the best choice.
Quality of Word Embeddings on Sentiment Analysis Tasks / Çano, Erion; Morisio, Maurizio. - ELETTRONICO. - 10260(2017), pp. 332-338. ((Intervento presentato al convegno NLDB 2017 22nd International Conference on Natural Language & Information Systems tenutosi a Liege, Belgium nel 21 - 23 June 2017 [10.1007/978-3-319-59569-6_42].
| Titolo: | Quality of Word Embeddings on Sentiment Analysis Tasks | |
| Autori: | ||
| Data di pubblicazione: | 2017 | |
| Abstract: | Word embeddings or distributed representations of words are being used in various applications l...ike machine translation, sentiment analysis, topic identification etc. Quality of word embeddings and performance of their applications depends on several factors like training method, corpus size and relevance etc. In this study we compare performance of a dozen of pretrained word embedding models on lyrics sentiment analysis and movie review polarity tasks. According to our results, Twitter Tweets is the best on lyrics sentiment analysis, whereas Google News and Common Crawl are the top performers on movie polarity analysis. Glove trained models slightly outrun those trained with Skipgram. Also, factors like topic relevance and size of corpus significantly impact the quality of the models. When medium or large-sized text sets are available, obtaining word embeddings from same training dataset is usually the best choice. | |
| ISBN: | 978-3-319-59569-6 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| ErionCanoWordEmbNLDB2017.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2668229
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