UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43957227" target="_blank" >RIV/49777513:23520/19:43957227 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.18653/v1/S18-1018" target="_blank" >http://dx.doi.org/10.18653/v1/S18-1018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/S18-1018" target="_blank" >10.18653/v1/S18-1018</a>

Result language

angličtina

Original language name

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Original language description

This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

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