{"doi":"10.17706/jsw.14.1.24-35","title":"EFTSA: Evaluation Framework for Twitter Sentiment Analysis","abstract":null,"journal":"Journal of Software","year":2019,"id":631335,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1636049,"name":"Abdullah Alsaeedi","orcid":null,"position":1,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"EFTSA: Evaluation Framework for Twitter Sentiment Analysis","abstract":"Sentiment analysis is a characteristic task that aims to detect the sentiment of opinions in content. Twitter sentiment analysis (TSA) is a promising field that has gained attention in the last decade. Investigators in the TSA field have faced difficulties comparing existing TSA techniques, as there is no agreed systematic framework. This means that the evaluation of existing techniques relies on selecting different datasets without meaningful justification. Another issue that arises when comparing different TSA techniques is that there are no unified metrics. Some researchers select classification accuracy and others choose recall, precision, and F-measure metrics. In this paper, we propose a framework called Evaluation Framework for Twitter Sentiment Analysis (EFTSA) for TSA evaluation based on individual or multiple datasets. This would help researchers compare their Twitter sentiment approaches against others.","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W2909085234","authors":[],"funders":[],"total_grants":0,"fwci":0.4236,"citation_percentile":0.69359275,"influential_citations":0,"citation_trend":[{"year":2020,"count":1},{"year":2021,"count":2},{"year":2024,"count":1}],"oa_status":"gold","license":null,"oa_locations":[{"url":"http://www.jsoftware.us/vol14/366-JSW15368.pdf","host_type":"journal"},{"url":"http://www.jsoftware.us/vol14/366-JSW15368.pdf","host_type":"publisher"},{"url":"https://doi.org/10.17706/jsw.14.1.24-35","host_type":"journal"}],"fields_of_study":["Sentiment Analysis and Opinion Mining","Advanced Text Analysis Techniques","Topic Modeling"],"mesh_terms":[],"keywords":["Computer science","Sentiment analysis","Social media","Information retrieval","Data science","Natural language processing","Artificial intelligence","World Wide Web"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T23:22:11.034383Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}