{"id":"https://openalex.org/W2735378217","doi":"https://doi.org/10.1109/icnidc.2016.7974614","title":"Study of feature word extraction and cluster in Chinese product reviews","display_name":"Study of feature word extraction and cluster in Chinese product reviews","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2735378217","doi":"https://doi.org/10.1109/icnidc.2016.7974614","mag":"2735378217"},"language":"en","primary_location":{"id":"doi:10.1109/icnidc.2016.7974614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnidc.2016.7974614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100758835","display_name":"Yanjun Shen","orcid":"https://orcid.org/0000-0003-2970-3108"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjun Shen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing 100876, China","Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100323016","display_name":"Guang Chen","orcid":"https://orcid.org/0000-0001-5611-6561"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing 100876, China","Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20448681,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"447","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.834937334060669},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7148123979568481},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6407245397567749},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.6400309801101685},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6272045373916626},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6223259568214417},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5776829123497009},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5585943460464478},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5342176556587219},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.515403151512146},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5110253095626831},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09621232748031616},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07941541075706482},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06740987300872803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.834937334060669},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7148123979568481},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6407245397567749},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.6400309801101685},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6272045373916626},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6223259568214417},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5776829123497009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5585943460464478},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5342176556587219},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.515403151512146},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5110253095626831},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09621232748031616},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07941541075706482},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06740987300872803},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnidc.2016.7974614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnidc.2016.7974614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2011430131","https://openalex.org/W2131571251","https://openalex.org/W2143027446","https://openalex.org/W2159457224","https://openalex.org/W2160660844","https://openalex.org/W4248506559"],"related_works":["https://openalex.org/W579810227","https://openalex.org/W2952780262","https://openalex.org/W2979495269","https://openalex.org/W2392917763","https://openalex.org/W2083429127","https://openalex.org/W4298130764","https://openalex.org/W4388713123","https://openalex.org/W2358855848","https://openalex.org/W2077104824","https://openalex.org/W2168778844"],"abstract_inverted_index":{"Evaluation":[0],"system":[1,19],"is":[2],"the":[3,29,39,43,48,57,60,76],"basis":[4],"of":[5,59],"product":[6,17,25],"reviews":[7],"mining.":[8],"This":[9,36],"paper":[10,37],"introduces":[11],"an":[12],"unsupervised":[13],"method":[14],"to":[15],"establish":[16],"evaluation":[18],"based":[20],"on":[21],"aspects.":[22,80],"We":[23],"extract":[24],"feature":[26,45],"words":[27],"with":[28],"syntax":[30],"parser":[31],"and":[32,51,70],"achieve":[33],"72.33%":[34],"F-value.":[35],"analyzes":[38],"mobile":[40,65],"reviews,":[41],"clusters":[42],"labeled":[44],"phrases":[46],"in":[47],"SemEval":[49],"task":[50],"achieves":[52],"71.5%":[53],"precision,":[54],"which":[55],"verifies":[56],"effectiveness":[58],"method.":[61],"Finally":[62],"we":[63],"make":[64],"features'":[66],"clustering":[67],"result":[68],"visible":[69],"draw":[71],"some":[72],"conclusions":[73],"by":[74],"analyzing":[75],"relationship":[77],"between":[78],"different":[79]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}