{"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/improving-the-accuracy-of-pre-trained-word","title":"Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis","arxiv_id":"1711.08609","date":"2017-11-23","proceeding":null,"authors":["Seyed Mahdi Rezaeinia","Ali Ghodsi","Rouhollah Rahmani"],"abstract":"Sentiment analysis is one of the well-known tasks and fast growing research\nareas in natural language processing (NLP) and text classifications. This\ntechnique has become an essential part of a wide range of applications\nincluding politics, business, advertising and marketing. There are various\ntechniques for sentiment analysis, but recently word embeddings methods have\nbeen widely used in sentiment classification tasks. Word2Vec and GloVe are\ncurrently among the most accurate and usable word embedding methods which can\nconvert words into meaningful vectors. However, these methods ignore sentiment\ninformation of texts and need a huge corpus of texts for training and\ngenerating exact vectors which are used as inputs of deep learning models. As a\nresult, because of the small size of some corpuses, researcher often have to\nuse pre-trained word embeddings which were trained on other large text corpus\nsuch as Google News with about 100 billion words. The increasing accuracy of\npre-trained word embeddings has a great impact on sentiment analysis research.\nIn this paper we propose a novel method, Improved Word Vectors (IWV), which\nincreases the accuracy of pre-trained word embeddings in sentiment analysis.\nOur method is based on Part-of-Speech (POS) tagging techniques, lexicon-based\napproaches and Word2Vec/GloVe methods. We tested the accuracy of our method via\ndifferent deep learning models and sentiment datasets. Our experiment results\nshow that Improved Word Vectors (IWV) are very effective for sentiment\nanalysis.","url_abs":"http://arxiv.org/abs/1711.08609v1","url_pdf":"http://arxiv.org/pdf/1711.08609v1.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":"improving-the-accuracy-of-pre-trained-word","repo_url":"https://github.com/PrashantRanjan09/Improved-Word-Embeddings","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"marketing","task_name":"Marketing"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"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":[{"method_slug":"glove","method_name":"GloVe"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}