Comment relevance classification in facebook

Chaya Liebeskind; Shmuel Liebeskind; Yaakov HaCohen-Kerner

doi:10.1007/978-3-319-77116-8_18

Comment relevance classification in facebook

Chaya Liebeskind, Shmuel Liebeskind, Yaakov HaCohen-Kerner

Jerusalem College of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Social posts and their comments are rich and interesting social data. In this study, we aim to classify comments as relevant or irrelevant to the content of their posts. Since the comments in social media are usually short, their bag-of-words (BoW) representations are highly sparse. We investigate four semantic vector representations for the relevance classification task. We investigate different types of large unlabeled data for learning the distributional representations. We also empirically demonstrate that expanding the input of the task to include the post text does not improve the classification performance over using only the comment text. We show that representing the comment in the post space is a cheap and good representation for comment relevance classification.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers
Editors	Alexander Gelbukh
Publisher	Springer Verlag
Pages	241-254
Number of pages	14
ISBN (Print)	9783319771151
DOIs	https://doi.org/10.1007/978-3-319-77116-8_18
State	Published - 2018
Externally published	Yes
Event	18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 - Budapest, Hungary Duration: 17 Apr 2017 → 23 Apr 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10762 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017
Country/Territory	Hungary
City	Budapest
Period	17/04/17 → 23/04/17

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

Comment relevance classification
Machine learning
Semantic analysis
Social media
Supervised learning

Access to Document

10.1007/978-3-319-77116-8_18

Cite this

Liebeskind, C., Liebeskind, S., & HaCohen-Kerner, Y. (2018). Comment relevance classification in facebook. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers (pp. 241-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10762 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-77116-8_18

Liebeskind, Chaya ; Liebeskind, Shmuel ; HaCohen-Kerner, Yaakov. / Comment relevance classification in facebook. Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. editor / Alexander Gelbukh. Springer Verlag, 2018. pp. 241-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{37e8514384c14c428b2ed7afd0899a86,

title = "Comment relevance classification in facebook",

abstract = "Social posts and their comments are rich and interesting social data. In this study, we aim to classify comments as relevant or irrelevant to the content of their posts. Since the comments in social media are usually short, their bag-of-words (BoW) representations are highly sparse. We investigate four semantic vector representations for the relevance classification task. We investigate different types of large unlabeled data for learning the distributional representations. We also empirically demonstrate that expanding the input of the task to include the post text does not improve the classification performance over using only the comment text. We show that representing the comment in the post space is a cheap and good representation for comment relevance classification.",

keywords = "Comment relevance classification, Machine learning, Semantic analysis, Social media, Supervised learning",

author = "Chaya Liebeskind and Shmuel Liebeskind and Yaakov HaCohen-Kerner",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 ; Conference date: 17-04-2017 Through 23-04-2017",

year = "2018",

doi = "10.1007/978-3-319-77116-8_18",

language = "אנגלית",

isbn = "9783319771151",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "241--254",

editor = "Alexander Gelbukh",

booktitle = "Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers",

address = "גרמניה",

}

Liebeskind, C, Liebeskind, S & HaCohen-Kerner, Y 2018, Comment relevance classification in facebook. in A Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10762 LNCS, Springer Verlag, pp. 241-254, 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017, Budapest, Hungary, 17/04/17. https://doi.org/10.1007/978-3-319-77116-8_18

Comment relevance classification in facebook. / Liebeskind, Chaya; Liebeskind, Shmuel; HaCohen-Kerner, Yaakov.
Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. ed. / Alexander Gelbukh. Springer Verlag, 2018. p. 241-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10762 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comment relevance classification in facebook

AU - Liebeskind, Chaya

AU - Liebeskind, Shmuel

AU - HaCohen-Kerner, Yaakov

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Social posts and their comments are rich and interesting social data. In this study, we aim to classify comments as relevant or irrelevant to the content of their posts. Since the comments in social media are usually short, their bag-of-words (BoW) representations are highly sparse. We investigate four semantic vector representations for the relevance classification task. We investigate different types of large unlabeled data for learning the distributional representations. We also empirically demonstrate that expanding the input of the task to include the post text does not improve the classification performance over using only the comment text. We show that representing the comment in the post space is a cheap and good representation for comment relevance classification.

AB - Social posts and their comments are rich and interesting social data. In this study, we aim to classify comments as relevant or irrelevant to the content of their posts. Since the comments in social media are usually short, their bag-of-words (BoW) representations are highly sparse. We investigate four semantic vector representations for the relevance classification task. We investigate different types of large unlabeled data for learning the distributional representations. We also empirically demonstrate that expanding the input of the task to include the post text does not improve the classification performance over using only the comment text. We show that representing the comment in the post space is a cheap and good representation for comment relevance classification.

KW - Comment relevance classification

KW - Machine learning

KW - Semantic analysis

KW - Social media

KW - Supervised learning

UR - http://www.scopus.com/inward/record.url?scp=85055685579&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-77116-8_18

DO - 10.1007/978-3-319-77116-8_18

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85055685579

SN - 9783319771151

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

SP - 241

EP - 254

BT - Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers

A2 - Gelbukh, Alexander

PB - Springer Verlag

T2 - 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017

Y2 - 17 April 2017 through 23 April 2017

ER -

Liebeskind C, Liebeskind S, HaCohen-Kerner Y. Comment relevance classification in facebook. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. Springer Verlag. 2018. p. 241-254. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-77116-8_18

Comment relevance classification in facebook

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this