Sentiment analysis from email pattern using feature selection algorithm
Today number of applications are available on mobile devices and computers for electronic mail (email) conversations. The demand for email communication is increasing day‐by‐day. Therefore the incoming and outgoing messages are also getting increased. However, extracting the sentiments from the emai...
Uloženo v:
| Vydáno v: | Expert systems Ročník 41; číslo 2 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford
Blackwell Publishing Ltd
01.02.2024
|
| Témata: | |
| ISSN: | 0266-4720, 1468-0394 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Today number of applications are available on mobile devices and computers for electronic mail (email) conversations. The demand for email communication is increasing day‐by‐day. Therefore the incoming and outgoing messages are also getting increased. However, extracting the sentiments from the emails is now demanding. Therefore in the proposed method, the pattern classification and sentiment clustering are carried out in two phases. Initially, the pattern classification is performed using support vector regression, then the sentiments from such classified patterns are clustered using a unsupervised fuzzy‐model‐based Gaussian clustering algorithm. Finally, the experimental analysis is performed in Python tool. The proposed sentiment clustering from email patterns has attained a better accuracy result of 97.13%, which is found higher than other existing techniques. Along with the parametric analysis, non‐parametric statistical analysis using the Wilcoxon rank‐sum test is also carried out to identify the proposed sentiment analysis architecture's effectiveness. |
|---|---|
| AbstractList | Today number of applications are available on mobile devices and computers for electronic mail (email) conversations. The demand for email communication is increasing day‐by‐day. Therefore the incoming and outgoing messages are also getting increased. However, extracting the sentiments from the emails is now demanding. Therefore in the proposed method, the pattern classification and sentiment clustering are carried out in two phases. Initially, the pattern classification is performed using support vector regression, then the sentiments from such classified patterns are clustered using a unsupervised fuzzy‐model‐based Gaussian clustering algorithm. Finally, the experimental analysis is performed in Python tool. The proposed sentiment clustering from email patterns has attained a better accuracy result of 97.13%, which is found higher than other existing techniques. Along with the parametric analysis, non‐parametric statistical analysis using the Wilcoxon rank‐sum test is also carried out to identify the proposed sentiment analysis architecture's effectiveness. |
| Author | Sharaff, Aakanksha Srinivasarao, Ulligaddala |
| Author_xml | – sequence: 1 givenname: Ulligaddala orcidid: 0000-0001-9199-2700 surname: Srinivasarao fullname: Srinivasarao, Ulligaddala email: usrinivasarao.phd2018.cs@nitrr.ac.in organization: National Institute of Technology Raipur – sequence: 2 givenname: Aakanksha orcidid: 0000-0001-5499-7289 surname: Sharaff fullname: Sharaff, Aakanksha organization: National Institute of Technology Raipur |
| BookMark | eNp9kE9LAzEQxYNUsK1e_AQBb8LWZLOb7B6l1CoUPFRBTyGbTmrK_qlJFt1vb2o9iTiHmcvvzbx5EzRquxYQuqRkRmPdwKcfZjQtuDhBY5rxIiGszEZoTFLOk0yk5AxNvN8RQqgQfIyWa2iDbWLDqlX14K3HxnUNhkbZGu9VCOBa3HvbbrEBFXoH2EMNOtiuxareds6Gt-YcnRpVe7j4mVP0fLd4mt8nq8flw_x2lWgWLyY6MyDKknEwxjCtq7zKxYZUwhiSM1NwpRTT-aYArRmUxOTGCFoWWRb5Da_YFF0d9-5d996DD3LX9S469zItaR5fznIaKXKktOu8d2CktkEdHAcX35KUyENc8hCX_I4rSq5_SfbONsoNf8P0CH_YGoZ_SLl4Wb8eNV-924Ae |
| CitedBy_id | crossref_primary_10_1007_s00500_023_08368_6 crossref_primary_10_1007_s42979_023_02018_2 crossref_primary_10_1111_exsy_13506 crossref_primary_10_1007_s11042_023_15206_2 crossref_primary_10_1007_s10115_024_02214_3 crossref_primary_10_1145_3749842 crossref_primary_10_1007_s11227_025_07658_0 |
| Cites_doi | 10.1007/s00366-018-0620-8 10.1145/3278293.3278301 10.1016/j.cose.2012.12.002 10.1016/j.neucom.2017.10.010 10.1140/epjp/i2018-11845-y 10.5815/ijmecs.2018.09.02 10.1109/INISTA.2017.8001177 10.22364/bjmc.2019.7.1.04 10.1109/ICITEED.2014.7007894 10.1007/978-981-13-1274-8_2 10.1007/978-981-13-1498-8_38 10.1111/2041-210X.12132 10.1007/s11227-018-2398-2 10.1007/s10586-017-1615-8 10.5121/ijcsit.2011.3112 10.1109/ICPC2T48082.2020.9071488 10.1109/NLPKE.2005.1598723 10.1093/jigpal/jzz073 10.1007/978-3-030-22871-2_39 10.1016/j.knosys.2016.05.032 10.4018/IJWLTT.2020040102 10.1007/s11042-016-3605-x 10.1109/MCI.2019.2954667 10.1109/35021BIGCOMP.2015.7072831 10.1016/j.neucom.2016.09.117 10.1177/0165551515587854 10.1007/s42452-019-0394-7 10.1109/BigData.2017.8258136 10.1007/978-3-030-18305-9_1 10.11591/ijece.v9i5.pp4452-4459 10.1080/01969722.2018.1448242 10.1016/j.eswa.2018.01.026 10.1007/s00500-017-2729-x 10.1109/ISKE.2015.91 10.1007/s12559-014-9298-4 10.1177/0165551515616310 10.1109/ICCIKE47802.2019.9004372 10.1109/CyberSecPODS.2019.8885143 |
| ContentType | Journal Article |
| Copyright | 2021 John Wiley & Sons Ltd. 2024 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: 2021 John Wiley & Sons Ltd. – notice: 2024 John Wiley & Sons, Ltd. |
| DBID | AAYXX CITATION 7SC 7TB 8FD F28 FR3 JQ2 L7M L~C L~D |
| DOI | 10.1111/exsy.12867 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1468-0394 |
| EndPage | n/a |
| ExternalDocumentID | 10_1111_exsy_12867 EXSY12867 |
| Genre | article |
| GroupedDBID | -~X .3N .4S .DC .GA .Y3 05W 0R~ 10A 1OB 1OC 29G 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6TJ 702 77I 77K 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 9M8 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABDPE ABEML ABLJU ABPVW ACAHQ ACBWZ ACCZN ACFBH ACGFS ACIWK ACNCT ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMHC ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEIGN AEIMD AEMOZ AENEX AEUYR AEYWJ AFBPY AFEBI AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AHEFC AHQJS AI. AIDQK AIDYY AIQQE AITYG AIURR AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CAG COF CS3 CWDTD D-E D-F DC6 DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBA EBR EBS EBU EDO EJD EMK EST ESX F00 F01 F04 FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MK~ MRFUL MRSTM MSFUL MSSTM MVM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QWB R.K RIWAO RJQFR ROL RX1 SAMSI SUPJJ TAE TH9 TN5 TUS UB1 VH1 W8V W99 WBKPD WH7 WIH WIK WLBEL WOHZO WQJ WXSBR WYISQ XG1 ZL0 ZZTAW ~02 ~IA ~WT AAYXX CITATION O8X 7SC 7TB 8FD F28 FR3 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c3017-c4fe79936efff3ccb5b57d0b7ff053f86aaa3c5d8ecc3e90f5ff719844fffd6b3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000714187800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0266-4720 |
| IngestDate | Sat Sep 06 22:30:51 EDT 2025 Tue Nov 18 22:39:05 EST 2025 Sat Nov 29 03:32:45 EST 2025 Thu Sep 25 07:34:13 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3017-c4fe79936efff3ccb5b57d0b7ff053f86aaa3c5d8ecc3e90f5ff719844fffd6b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9199-2700 0000-0001-5499-7289 |
| PQID | 2915146451 |
| PQPubID | 32130 |
| PageCount | 22 |
| ParticipantIDs | proquest_journals_2915146451 crossref_citationtrail_10_1111_exsy_12867 crossref_primary_10_1111_exsy_12867 wiley_primary_10_1111_exsy_12867_EXSY12867 |
| PublicationCentury | 2000 |
| PublicationDate | February 2024 2024-02-00 20240201 |
| PublicationDateYYYYMMDD | 2024-02-01 |
| PublicationDate_xml | – month: 02 year: 2024 text: February 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Expert systems |
| PublicationYear | 2024 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | 2019; 7 2019; 9 2017; 2 2016; 108 2019; 1 2019; 35 2017; 43 2018a; 49 2020; 15 2005 2020; 76 2018; 22 2011; 3 2015; 7 2018; 133 2018; 275 2014; 5 2018; 4 2019; 22 2013; 34 2020 2015; 42 2017; 76 2020; 28 2019 2018 2017 2016 2017; 261 2015 2018b; 99 2014 2017; 263 2018; 10 e_1_2_12_4_1 e_1_2_12_3_1 e_1_2_12_6_1 e_1_2_12_5_1 Liu S. (e_1_2_12_29_1) 2016 e_1_2_12_19_1 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_16_1 e_1_2_12_38_1 e_1_2_12_39_1 e_1_2_12_42_1 e_1_2_12_20_1 e_1_2_12_41_1 e_1_2_12_44_1 e_1_2_12_22_1 e_1_2_12_43_1 e_1_2_12_23_1 e_1_2_12_24_1 e_1_2_12_25_1 e_1_2_12_26_1 e_1_2_12_40_1 Hassan M. K. (e_1_2_12_18_1) 2017 e_1_2_12_27_1 e_1_2_12_28_1 e_1_2_12_30_1 e_1_2_12_31_1 e_1_2_12_32_1 e_1_2_12_33_1 e_1_2_12_34_1 e_1_2_12_35_1 e_1_2_12_36_1 e_1_2_12_15_1 e_1_2_12_14_1 e_1_2_12_13_1 Schofield A. (e_1_2_12_37_1) 2017 e_1_2_12_12_1 e_1_2_12_8_1 e_1_2_12_11_1 e_1_2_12_7_1 e_1_2_12_10_1 e_1_2_12_9_1 Jawale D. S. (e_1_2_12_21_1) 2018; 4 |
| References_xml | – volume: 99 start-page: 1 year: 2018b end-page: 11 article-title: Discovering sentiment sequence within email data through trajectory representation publication-title: Expert Systems with Applications – start-page: 328 year: 2019 end-page: 333 article-title: Sentiment Analysis using Unlabeled Email data publication-title: 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) – start-page: 1907 year: 2017 end-page: 1916 – volume: 2 start-page: 432 year: 2017 end-page: 436 – volume: 35 start-page: 619 issue: 2 year: 2019 end-page: 626 article-title: A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates publication-title: Engineering with Computers – volume: 34 start-page: 123 year: 2013 end-page: 139 article-title: Phishing detection and impersonated entity discovery using conditional random field and latent Dirichlet allocation publication-title: Computers & Security – start-page: 312 year: 2017 end-page: 315 – start-page: 324 year: 2015 end-page: 330 – volume: 261 start-page: 217 year: 2017 end-page: 230 article-title: Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis publication-title: Neurocomputing – volume: 15 start-page: 19 issue: 2 year: 2020 end-page: 33 article-title: ML‐EC2: An algorithm for multi‐label email classification using clustering publication-title: International Journal of Web‐Based Learning and Teaching Technologies (IJWLTT) – volume: 133 start-page: 1 issue: 1 year: 2018 end-page: 9 article-title: Support vector regression methodology for estimating global solar radiation in Algeria publication-title: The European Physical Journal Plus – start-page: 573 year: 2019 end-page: 592 – start-page: 21 year: 2019 end-page: 33 – start-page: 194 year: 2015 end-page: 201 article-title: Semi‐supervised microblog sentiment analysis using social relation and text similarity – volume: 76 start-page: 10191 issue: 7 year: 2017 end-page: 10205 article-title: A new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classification publication-title: Multimedia Tools and Applications – volume: 22 start-page: 33 issue: 1 year: 2019 end-page: 46 article-title: Visual and textual features based email spam classification using S‐cuckoo search and hybrid kernel support vector machine publication-title: Cluster Computing – volume: 1 start-page: 390 issue: 5 year: 2019 article-title: Whale optimization algorithm‐based email spam feature selection method using rotation forest algorithm for classification publication-title: SN Applied Sciences – volume: 263 year: 2017 – volume: 7 start-page: 47 issue: 1 year: 2019 end-page: 60 article-title: SVM and k‐means hybrid method for textual data sentiment analysis publication-title: Baltic Journal of Modern Computing – volume: 15 start-page: 64 issue: 1 year: 2020 end-page: 75 article-title: How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [application notes] publication-title: IEEE Computational Intelligence Magazine – volume: 22 start-page: 7281 issue: 21 year: 2018 end-page: 7291 article-title: Sentiment analysis and spam detection in short informal text using learning classifier systems publication-title: Soft Computing – start-page: 1 year: 2014 end-page: 4 – volume: 76 start-page: 4414 issue: 6 year: 2020 end-page: 4429 article-title: A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization‐based long short‐term memory neural networks publication-title: The Journal of Supercomputing – volume: 49 start-page: 181 issue: 3 year: 2018a end-page: 199 article-title: Email sentiment analysis through k‐means labeling and support vector machine classification publication-title: Cybernetics and Systems – year: 2019 article-title: Identifying categorical terms based on latent Dirichlet allocation for email categorization publication-title: Emerging Technologies in Data Mining and Information Security – volume: 3 start-page: 173 issue: 1 year: 2011 end-page: 184 article-title: Machine learning methods for spam e‐mail classification publication-title: International Journal of Computer Science & Information Technology (IJCSIT) – volume: 7 start-page: 369 issue: 3 year: 2015 end-page: 380 article-title: Word polarity disambiguation using bayesian model and opinion‐level features publication-title: Cognitive Computation – start-page: 1 year: 2019 end-page: 2 article-title: Classifying phishing email using machine learning and deep learning publication-title: 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) – volume: 108 start-page: 92 year: 2016 end-page: 101 article-title: Contextual sentiment analysis for social media genres publication-title: Knowledge‐Based Systems – start-page: 363 year: 2016 end-page: 371 article-title: Sentiment clustering with topic and temporal information from large email dataset – volume: 9 start-page: 4452 issue: 5 year: 2019 end-page: 4459 article-title: Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments publication-title: International Journal of Electrical and Computer Engineering (IJECE) – volume: 28 start-page: 83 year: 2020 end-page: 94 article-title: Novel email spam detection method using sentiment analysis and personality recognition publication-title: Logic Journal of the IGPL – volume: 4 start-page: 2828 issue: 2 year: 2018 end-page: 2832 article-title: Hybrid spam detection using machine learning publication-title: International Journal of Advance Research, Ideas and Innovations in Technology – start-page: 316 year: 2020 end-page: 320 article-title: Towards classification of email through selection of informative features publication-title: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T) – year: 2017 – volume: 275 start-page: 1662 year: 2018 end-page: 1673 article-title: Semi‐supervised learning for big social data analysis publication-title: Neurocomputing – volume: 10 start-page: 11 issue: 9 year: 2018 end-page: 19 article-title: Developing an efficient text pre‐processing method with sparse generative naive Bayes for text mining publication-title: International Journal of Modern Education and Computer Science – volume: 42 start-page: 200 issue: 2 year: 2015 end-page: 212 article-title: Email thread identification using latent Dirichlet allocation and non‐negative matrix factorization based clustering techniques publication-title: Journal of Information Science – volume: 43 start-page: 75 issue: 1 year: 2017 end-page: 87 article-title: SMS spam filtering and thread identification using bi‐level text classification and clustering techniques publication-title: Journal of Information Science – volume: 5 start-page: 125 issue: 2 year: 2014 end-page: 131 article-title: Personal messages reduce vandalism and theft of unattended scientific equipment publication-title: Methods in Ecology and Evolution – start-page: 3 year: 2019 end-page: 15 – start-page: 142 year: 2005 end-page: 146 – start-page: 12 year: 2018 end-page: 16 article-title: A novel feature hashing with efficient collision resolution for bag‐of‐words representation of text data publication-title: Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval – ident: e_1_2_12_15_1 doi: 10.1007/s00366-018-0620-8 – start-page: 363 volume-title: Proceedings of the 30th Pacific Asia conference on language, information and computation: posters year: 2016 ident: e_1_2_12_29_1 – ident: e_1_2_12_13_1 doi: 10.1145/3278293.3278301 – ident: e_1_2_12_36_1 doi: 10.1016/j.cose.2012.12.002 – ident: e_1_2_12_20_1 doi: 10.1016/j.neucom.2017.10.010 – ident: e_1_2_12_16_1 doi: 10.1140/epjp/i2018-11845-y – ident: e_1_2_12_34_1 doi: 10.5815/ijmecs.2018.09.02 – volume: 4 start-page: 2828 issue: 2 year: 2018 ident: e_1_2_12_21_1 article-title: Hybrid spam detection using machine learning publication-title: International Journal of Advance Research, Ideas and Innovations in Technology – ident: e_1_2_12_19_1 doi: 10.1109/INISTA.2017.8001177 – ident: e_1_2_12_22_1 doi: 10.22364/bjmc.2019.7.1.04 – start-page: 042090 volume-title: IOP Conference Series: Materials Science and Engineering year: 2017 ident: e_1_2_12_18_1 – ident: e_1_2_12_17_1 doi: 10.1109/ICITEED.2014.7007894 – start-page: 432 volume-title: Proceedings of the 15th conference of the European chapter of the Association for Computational Linguistics year: 2017 ident: e_1_2_12_37_1 – ident: e_1_2_12_3_1 doi: 10.1007/978-981-13-1274-8_2 – ident: e_1_2_12_39_1 doi: 10.1007/978-981-13-1498-8_38 – ident: e_1_2_12_12_1 doi: 10.1111/2041-210X.12132 – ident: e_1_2_12_31_1 – ident: e_1_2_12_4_1 doi: 10.1007/s11227-018-2398-2 – ident: e_1_2_12_23_1 doi: 10.1007/s10586-017-1615-8 – ident: e_1_2_12_8_1 doi: 10.5121/ijcsit.2011.3112 – ident: e_1_2_12_41_1 doi: 10.1109/ICPC2T48082.2020.9071488 – ident: e_1_2_12_25_1 doi: 10.1109/NLPKE.2005.1598723 – ident: e_1_2_12_14_1 doi: 10.1093/jigpal/jzz073 – ident: e_1_2_12_24_1 doi: 10.1007/978-3-030-22871-2_39 – ident: e_1_2_12_32_1 doi: 10.1016/j.knosys.2016.05.032 – ident: e_1_2_12_40_1 doi: 10.4018/IJWLTT.2020040102 – ident: e_1_2_12_6_1 doi: 10.1007/s11042-016-3605-x – ident: e_1_2_12_2_1 doi: 10.1109/MCI.2019.2954667 – ident: e_1_2_12_30_1 doi: 10.1109/35021BIGCOMP.2015.7072831 – ident: e_1_2_12_35_1 doi: 10.1016/j.neucom.2016.09.117 – ident: e_1_2_12_38_1 doi: 10.1177/0165551515587854 – ident: e_1_2_12_42_1 doi: 10.1007/s42452-019-0394-7 – ident: e_1_2_12_10_1 doi: 10.1109/BigData.2017.8258136 – ident: e_1_2_12_44_1 doi: 10.1007/978-3-030-18305-9_1 – ident: e_1_2_12_11_1 doi: 10.11591/ijece.v9i5.pp4452-4459 – ident: e_1_2_12_27_1 doi: 10.1080/01969722.2018.1448242 – ident: e_1_2_12_28_1 doi: 10.1016/j.eswa.2018.01.026 – ident: e_1_2_12_7_1 doi: 10.1007/s00500-017-2729-x – ident: e_1_2_12_26_1 doi: 10.1109/ISKE.2015.91 – ident: e_1_2_12_43_1 doi: 10.1007/s12559-014-9298-4 – ident: e_1_2_12_33_1 doi: 10.1177/0165551515616310 – ident: e_1_2_12_5_1 doi: 10.1109/ICCIKE47802.2019.9004372 – ident: e_1_2_12_9_1 doi: 10.1109/CyberSecPODS.2019.8885143 |
| SSID | ssj0001776 |
| Score | 2.3668664 |
| Snippet | Today number of applications are available on mobile devices and computers for electronic mail (email) conversations. The demand for email communication is... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| SubjectTerms | Algorithms Clustering Data mining Electronic mail email pattern Feature selection feature selection unsupervised fuzzy Gaussian mixture model Mobile computing Parametric analysis Pattern analysis Pattern classification Sentiment analysis sentiment clustering Statistical analysis Support vector machines support vector regression |
| Title | Sentiment analysis from email pattern using feature selection algorithm |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fexsy.12867 https://www.proquest.com/docview/2915146451 |
| Volume | 41 |
| WOSCitedRecordID | wos000714187800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1468-0394 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001776 issn: 0266-4720 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFH_o9ODF-YnTKQG9KFT6kTYteBHd9CBD1ME8lZcsmYN9sU7R_94kbTcFEcRbD6-hvM9f0pffAzhRwrf9fg4NJDq0i66DMXqONHR1TNdf30M7bIK1WnGnk9wvwUV5Fybnh5gfuJnIsPnaBDjy7EuQy_fs41xn14gtw4qvHZdWYOX6odm-m2dij9nhcnqbETmU-W5BT2o6eRZvfy9IC5T5FavaYtOs_u8zN2C9AJnkMveKTViSoy2olgMcSBHP23DzaHqFzPkgwYKchJj7JkQOsT8gE8u9OSKmN75HlLQcoCSzk3O0OQkOeuNpf_Yy3IF2s_F0desUkxUcEZiyJKiSTCOTSCqlAiF4yEPWdTlTSgeliiNEDETYjbWBA5m4KlSKeUlMqZbvRjzYhcpoPJJ7QHjAOdLE5RrX0NB1kQuhMRxiKBUK4dfgtFRvKgracTP9YpCW2w-jodRqqAbHc9lJTrbxo1S9tFJaBFyW-omGLuYvrVeDM2uPX1ZIG53HZ_u0_xfhA1jzNaTJe7brUJlNX-UhrIq3WT-bHhXO9wk7bOEz |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JSwMxFH5oK-jFumK1akAvCiOzZCYzR9HWirWIbaGehiRNaqEbbRX99yZpuggiiLc5vAnDW79kXr4HcC65b_r9HBwI6uAWdR0aU88Rmq6OqPrre9QMmyDVatxsJk-2N0ffhZnyQ8wP3HRkmHytA1wfSC9FufgYf16p9BqRVchi5UdhBrK3z6VGZZ6KPWKmy6l9RuRg4ruWn1S38ize_l6RFjBzGayaalPK_fM7t2DTwkx0PfWLbVgR_R3IzUY4IBvRu3BX091C-oQQUUtPgvSNEyR6tNNFQ8O-2Ue6O76NpDAsoGhsZucogyLabQ9Gnclrbw8apWL9puzY2QoOD3Rh4lgKorBJJKSUAecsZCFpuYxIqcJSxhGlNOBhK1YmDkTiylBK4iUxxkq-FbFgHzL9QV8cAGIBYxQnLlPIBoeuSxnnCsVRGgpJOffzcDHTb8ot8bief9FNZxsQraHUaCgPZ3PZ4ZRu40epwsxMqQ25ceonCrzo_7ReHi6NQX5ZIS02ay_m6fAvwqewXq4_VtLKffXhCDZ8BXCmHdwFyExGb-IY1vj7pDMenVhP_AKwEuUj |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEB60inixPrFaNaAXhZV9ZDe7R7GtiqUUa6GeliSb1EJftFX035uk6UMQQbztYTYsmXwzX7KTbwAuJPdNvZ-DA0EdnFHXoTH1HKHl6ojKv75HTbMJUqvFrVZSt7U5-i7MVB9ifuCmkWHitQa4GGZyCeXiY_x5rcJrRFZhDYdJpHC5VnqqNKvzUOwR011O7TMiBxPftfqkupRn8fb3jLSgmctk1WSbSv6f37kNW5ZmopvputiBFdHfhfyshQOyiN6Du4auFtInhIhaeRKkb5wg0aOdLhoa9c0-0tXxbSSFUQFFY9M7RzkU0W57MOpMXnv70KyUn2_vHdtbweGBTkwcS0EUN4mElDLgnIUsJJnLiJQKljKOKKUBD7NYuTgQiStDKYmXxBgr-yxiwQHk-oO-OATEAsYoTlymmA0OXZcyzhWLozQUknLuF-ByNr8pt8Ljuv9FN51tQPQMpWaGCnA-tx1O5TZ-tCrO3JRayI1TP1HkRf-n9QpwZRzyywhpudV4MU9HfzE-g416qZJWH2qPx7DpK34zLeAuQm4yehMnsM7fJ53x6NQuxC9kneSe |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sentiment+analysis+from+email+pattern+using+feature+selection+algorithm&rft.jtitle=Expert+systems&rft.au=Srinivasarao%2C+Ulligaddala&rft.au=Sharaff%2C+Aakanksha&rft.date=2024-02-01&rft.issn=0266-4720&rft.eissn=1468-0394&rft.volume=41&rft.issue=2&rft_id=info:doi/10.1111%2Fexsy.12867&rft.externalDBID=n%2Fa&rft.externalDocID=10_1111_exsy_12867 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0266-4720&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0266-4720&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0266-4720&client=summon |