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- W4289763313 abstract "The web is loaded with textual content, and Natural Language Processing is a standout amongst the most vital fields in Machine Learning. But when data is huge simple Machine Learning algorithms are not able to handle it and that is when Deep Learning comes into play which based on Neural Networks. However since neural networks cannot process raw text, we have to change over them through some diverse strategies of word embedding. This paper demonstrates those distinctive word embedding strategies implemented on an Amazon Review Dataset, which has two sentiments to be classified: Happy and Unhappy based on numerous customer reviews. Moreover we demonstrate the distinction in accuracy with a discourse about which word embedding to apply when." @default.
- W4289763313 created "2022-08-04" @default.
- W4289763313 creator A5065005298 @default.
- W4289763313 date "2018-07-05" @default.
- W4289763313 modified "2023-09-26" @default.
- W4289763313 title "A Review of Different Word Embeddings for Sentiment Classification using Deep Learning" @default.
- W4289763313 doi "https://doi.org/10.48550/arxiv.1807.02471" @default.
- W4289763313 hasPublicationYear "2018" @default.
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