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Extraction of explicit consumer intentions from social network messages. / Pimenov, Ivan; Salomatina, Natalia.

Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. ed. / Alexander Panchenko; Wil M. van der Aalst; Michael Khachay; Panos M. Pardalos; Vladimir Batagelj; Natalia Loukachevitch; Goran Glavaš; Dmitry I. Ignatov; Sergei O. Kuznetsov; Olessia Koltsova; Irina A. Lomazova; Andrey V. Savchenko; Amedeo Napoli; Marcello Pelillo. Springer-Verlag GmbH and Co. KG, 2018. p. 127-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11179 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Pimenov, I & Salomatina, N 2018, Extraction of explicit consumer intentions from social network messages. in A Panchenko, WM van der Aalst, M Khachay, PM Pardalos, V Batagelj, N Loukachevitch, G Glavaš, DI Ignatov, SO Kuznetsov, O Koltsova, IA Lomazova, AV Savchenko, A Napoli & M Pelillo (eds), Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11179 LNCS, Springer-Verlag GmbH and Co. KG, pp. 127-133, 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, Moscow, Russian Federation, 05.07.2018. https://doi.org/10.1007/978-3-030-11027-7_13

APA

Pimenov, I., & Salomatina, N. (2018). Extraction of explicit consumer intentions from social network messages. In A. Panchenko, W. M. van der Aalst, M. Khachay, P. M. Pardalos, V. Batagelj, N. Loukachevitch, G. Glavaš, D. I. Ignatov, S. O. Kuznetsov, O. Koltsova, I. A. Lomazova, A. V. Savchenko, A. Napoli, & M. Pelillo (Eds.), Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers (pp. 127-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11179 LNCS). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-030-11027-7_13

Vancouver

Pimenov I, Salomatina N. Extraction of explicit consumer intentions from social network messages. In Panchenko A, van der Aalst WM, Khachay M, Pardalos PM, Batagelj V, Loukachevitch N, Glavaš G, Ignatov DI, Kuznetsov SO, Koltsova O, Lomazova IA, Savchenko AV, Napoli A, Pelillo M, editors, Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Springer-Verlag GmbH and Co. KG. 2018. p. 127-133. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11027-7_13

Author

Pimenov, Ivan ; Salomatina, Natalia. / Extraction of explicit consumer intentions from social network messages. Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. editor / Alexander Panchenko ; Wil M. van der Aalst ; Michael Khachay ; Panos M. Pardalos ; Vladimir Batagelj ; Natalia Loukachevitch ; Goran Glavaš ; Dmitry I. Ignatov ; Sergei O. Kuznetsov ; Olessia Koltsova ; Irina A. Lomazova ; Andrey V. Savchenko ; Amedeo Napoli ; Marcello Pelillo. Springer-Verlag GmbH and Co. KG, 2018. pp. 127-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{14a9443112c0455da73866be33eb346e,
title = "Extraction of explicit consumer intentions from social network messages",
abstract = "In this paper we address the problem of automatic extraction of facts from Russian texts. The facts under examination are the intentions of social network users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows distinguishing between various intentional blocks: objects, their different properties and emphatic constructions. Pre-processing of the training set includes lemmatization and grammatical tagging with PyMorphy2. Then, on the material of the training set, a directed graph is constructed. Each node in this graph corresponds to an intentional block, including information about its expertly-assigned intentional tag, grammatical and/or lexical properties of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set includes both messages containing non-consumer intentions or no intentions at all. The precision and recall of intention extraction with macro average is 82% and 74% respectively.",
keywords = "Directed graph, Fact extraction, Intention, Intention marker, Machine learning with annotation",
author = "Ivan Pimenov and Natalia Salomatina",
note = "Funding Information: Acknowledgements. The study was supported by the Russian Academy of Science (the Program of Basic Research, project 0314-2016-0015). Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018 ; Conference date: 05-07-2018 Through 07-07-2018",
year = "2018",
doi = "10.1007/978-3-030-11027-7_13",
language = "English",
isbn = "9783030110260",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "127--133",
editor = "Alexander Panchenko and {van der Aalst}, {Wil M.} and Michael Khachay and Pardalos, {Panos M.} and Vladimir Batagelj and Natalia Loukachevitch and Goran Glava{\v s} and Ignatov, {Dmitry I.} and Kuznetsov, {Sergei O.} and Olessia Koltsova and Lomazova, {Irina A.} and Savchenko, {Andrey V.} and Amedeo Napoli and Marcello Pelillo",
booktitle = "Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers",
address = "Germany",

}

RIS

TY - GEN

T1 - Extraction of explicit consumer intentions from social network messages

AU - Pimenov, Ivan

AU - Salomatina, Natalia

N1 - Funding Information: Acknowledgements. The study was supported by the Russian Academy of Science (the Program of Basic Research, project 0314-2016-0015). Publisher Copyright: © Springer Nature Switzerland AG 2018. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2018

Y1 - 2018

N2 - In this paper we address the problem of automatic extraction of facts from Russian texts. The facts under examination are the intentions of social network users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows distinguishing between various intentional blocks: objects, their different properties and emphatic constructions. Pre-processing of the training set includes lemmatization and grammatical tagging with PyMorphy2. Then, on the material of the training set, a directed graph is constructed. Each node in this graph corresponds to an intentional block, including information about its expertly-assigned intentional tag, grammatical and/or lexical properties of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set includes both messages containing non-consumer intentions or no intentions at all. The precision and recall of intention extraction with macro average is 82% and 74% respectively.

AB - In this paper we address the problem of automatic extraction of facts from Russian texts. The facts under examination are the intentions of social network users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows distinguishing between various intentional blocks: objects, their different properties and emphatic constructions. Pre-processing of the training set includes lemmatization and grammatical tagging with PyMorphy2. Then, on the material of the training set, a directed graph is constructed. Each node in this graph corresponds to an intentional block, including information about its expertly-assigned intentional tag, grammatical and/or lexical properties of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set includes both messages containing non-consumer intentions or no intentions at all. The precision and recall of intention extraction with macro average is 82% and 74% respectively.

KW - Directed graph

KW - Fact extraction

KW - Intention

KW - Intention marker

KW - Machine learning with annotation

UR - http://www.scopus.com/inward/record.url?scp=85059947568&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-11027-7_13

DO - 10.1007/978-3-030-11027-7_13

M3 - Conference contribution

AN - SCOPUS:85059947568

SN - 9783030110260

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 127

EP - 133

BT - Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers

A2 - Panchenko, Alexander

A2 - van der Aalst, Wil M.

A2 - Khachay, Michael

A2 - Pardalos, Panos M.

A2 - Batagelj, Vladimir

A2 - Loukachevitch, Natalia

A2 - Glavaš, Goran

A2 - Ignatov, Dmitry I.

A2 - Kuznetsov, Sergei O.

A2 - Koltsova, Olessia

A2 - Lomazova, Irina A.

A2 - Savchenko, Andrey V.

A2 - Napoli, Amedeo

A2 - Pelillo, Marcello

PB - Springer-Verlag GmbH and Co. KG

T2 - 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018

Y2 - 5 July 2018 through 7 July 2018

ER -

ID: 27889664