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- W3128656532 abstract "Sentiment lexicon is widely used resource which plays an important role in sentiment analysis tasks. Sentiment lexicon learning using minimum labeled resources for multiple domains is a challenging task. Multiple domains with similar characteristics are considered as same genre domains in our work. We present an unsupervised sentiment lexicon learning model for same-genre multiple domains. The model uses pre-learned genre-based polarity seed words as only labeled input and explore sentiment lexicon learning using different unsupervised word-embedding approaches. The pretrained embedding provides generalized word vectors, so our approach explores it further by creating domain-based word-embeddings. The experimentation involves sentiment lexicon learning using Word2Vec, GloVe, and fastText word-embeddings. The experiments are conducted on 18 same genre domains. The different word-embedding sentiment lexicon learning model results are compared across genre-base domains using standard evaluation metrics. The proposed model performs good in comparison to the existing models from the literature." @default.
- W3128656532 created "2021-02-15" @default.
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- W3128656532 date "2021-01-01" @default.
- W3128656532 modified "2023-09-25" @default.
- W3128656532 title "Multidomain Sentiment Lexicon Learning Using Genre-Seed Embeddings" @default.
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- W3128656532 doi "https://doi.org/10.1007/978-981-16-0401-0_17" @default.
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