{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["19(2)"],"submitter":["A Semary N"],"pubmed_abstract":["A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor,"],"journal":["PloS one"],"pagination":["e0294968"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10866497"],"repository":["biostudies-literature"],"pubmed_title":["Enhancing machine learning-based sentiment analysis through feature extraction techniques."],"pmcid":["PMC10866497"],"pubmed_authors":["Ahmed W","Amin K","Plawiak P","Hammad M","A Semary N"],"additional_accession":[]},"is_claimable":false,"name":"Enhancing machine learning-based sentiment analysis through feature extraction techniques.","description":"A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor,","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-06-11T06:08:37.274Z","creation":"2025-04-21T15:17:28.475Z"},"accession":"S-EPMC10866497","cross_references":{"pubmed":["38354193"],"doi":["10.1371/journal.pone.0294968"]}}