UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F14%3A43922746" target="_blank" >RIV/49777513:23520/14:43922746 - 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: Machine Learning Approach to Aspect-Based Sentiment Analysis
Original language description
This paper describes our system participating in the aspect-based sentiment analysis task of Semeval 2014. The goal was to identify the aspects of given target entities and the sentiment expressed towards each aspect. We firstly introduce a system basedon supervised machine learning, which is strictly constrained and uses the training data as the only source of information. This system is then extended by unsupervised methods for latent semantics discovery (LDA and semantic spaces) as well as the approach based on sentiment vocabularies. The evaluation was done on two domains, restaurants and laptops. We show that our approach leads to very promising results.
Czech name
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Czech description
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Project
<a href="/en/project/ED1.1.00%2F02.0090" target="_blank" >ED1.1.00/02.0090: NTIS - New Technologies for 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
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Workshop on Semantic Evaluation (SemEval 2014)
ISBN
978-1-941643-24-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
817-822
Publisher name
Association for Computational Linguistics and Dublin City University
Place of publication
Stroudsburg PA
Event location
Dublin, Ireland
Event date
Aug 22, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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