UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F16%3A43929283" target="_blank" >RIV/49777513:23520/16:43929283 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination

Original language description

We present a novel method for determining sentiment intensity. The main goal is to assign a phrase a score from 0 to 1 which indicates the strength of its association with positive sentiment. The proposed model uses a rich set of features with Gaussian processes regression model that computes the final score. The system was evaluated on the data from 7th task of SemEval 2016. Our regression model trained on the development data reached Kendall rank correlation of 0.659 on general English phrases and 0.414 on English Twitter test data.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Workshop SemEval-2016

ISBN

978-1-941643-95-2

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

481-485

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg

Event location

San Diego

Event date

Jun 16, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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