LTRC IIITH at IBEREVAL 2017: Stance and gender detection in tweets on catalan independence

Sahil Swami; Ankush Khandelwal; Manish Shrivastava; Syed Sarfaraz Akhtar

LTRC IIITH at IBEREVAL 2017: Stance and gender detection in tweets on catalan independence

Sahil Swami, Ankush Khandelwal, Manish Shrivastava, Syed Sarfaraz Akhtar

Computer Science and Engineering

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence [1]. We developed a supervised system using Support Vector Machines with ra- dial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) of 0.46 for stance detection in both Spanish and Catalan and an accuracy of 64.85% and 44.59% for Gender detection in Spanish and Catalan respectively.

Original language	English (US)
Pages (from-to)	199-203
Number of pages	5
Journal	CEUR Workshop Proceedings
Volume	1881
State	Published - Jan 1 2017
Event	2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2017 - Murcia, Spain Duration: Sep 19 2017 → …

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title = "LTRC IIITH at IBEREVAL 2017: Stance and gender detection in tweets on catalan independence",

abstract = "We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence [1]. We developed a supervised system using Support Vector Machines with ra- dial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) of 0.46 for stance detection in both Spanish and Catalan and an accuracy of 64.85% and 44.59% for Gender detection in Spanish and Catalan respectively.",

author = "Sahil Swami and Ankush Khandelwal and Manish Shrivastava and Akhtar, {Syed Sarfaraz}",

year = "2017",

month = jan,

day = "1",

language = "English (US)",

volume = "1881",

pages = "199--203",

journal = "CEUR Workshop Proceedings",

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note = "2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2017 ; Conference date: 19-09-2017",

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T1 - LTRC IIITH at IBEREVAL 2017

T2 - 2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2017

AU - Swami, Sahil

AU - Khandelwal, Ankush

AU - Shrivastava, Manish

AU - Akhtar, Syed Sarfaraz

PY - 2017/1/1

Y1 - 2017/1/1

N2 - We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence [1]. We developed a supervised system using Support Vector Machines with ra- dial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) of 0.46 for stance detection in both Spanish and Catalan and an accuracy of 64.85% and 44.59% for Gender detection in Spanish and Catalan respectively.

AB - We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence [1]. We developed a supervised system using Support Vector Machines with ra- dial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) of 0.46 for stance detection in both Spanish and Catalan and an accuracy of 64.85% and 44.59% for Gender detection in Spanish and Catalan respectively.

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M3 - Conference article

AN - SCOPUS:85027857143

SN - 1613-0073

VL - 1881

SP - 199

EP - 203

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 19 September 2017

ER -

LTRC IIITH at IBEREVAL 2017: Stance and gender detection in tweets on catalan independence

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