{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-review-of-different-word-embeddings-for","title":"A Review of Different Word Embeddings for Sentiment Classification using Deep Learning","arxiv_id":"1807.02471","date":"2018-07-05","proceeding":null,"authors":["Debadri Dutta"],"abstract":"The web is loaded with textual content, and Natural Language Processing is a\nstandout amongst the most vital fields in Machine Learning. But when data is\nhuge simple Machine Learning algorithms are not able to handle it and that is\nwhen Deep Learning comes into play which based on Neural Networks. However\nsince neural networks cannot process raw text, we have to change over them\nthrough some diverse strategies of word embedding. This paper demonstrates\nthose distinctive word embedding strategies implemented on an Amazon Review\nDataset, which has two sentiments to be classified: Happy and Unhappy based on\nnumerous customer reviews. Moreover we demonstrate the distinction in accuracy\nwith a discourse about which word embedding to apply when.","url_abs":"http://arxiv.org/abs/1807.02471v1","url_pdf":"http://arxiv.org/pdf/1807.02471v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-review-of-different-word-embeddings-for","repo_url":"https://github.com/debadridtt/A-Review-of-Different-Word-Embeddings-for-Sentiment-Classification-using-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}