Sentiment Analysis Based on Support Vector Machine and Big Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120166" target="_blank" >RIV/00216305:26220/16:PU120166 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7760939" target="_blank" >https://ieeexplore.ieee.org/document/7760939</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2016.7760939" target="_blank" >10.1109/TSP.2016.7760939</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sentiment Analysis Based on Support Vector Machine and Big Data

Popis výsledku v původním jazyce

This paper deals with sentiment analysis in text documents, especially text valence detection. The proposed solution is based on Support Vector Machines classifier. This classifier was trained with huge amount of data and complex word combinations were analysed. For this purpose distributed learning on 112 processors was used. Datasets used for training and testing were automatically obtained from real user feedback on products from different web pages (and different product segments). The proposed solution has been evaluated with different languages – English, German, Czech and Spanish. This paper improves accuracy achieved with the Big Data approach about 11%. The best accuracy achieved in this work was 95.31% for recognition of positive and negative text valence. The described learning is fully automatic, can be applied to any language and no complicated preprocessing is needed.

Název v anglickém jazyce

Sentiment Analysis Based on Support Vector Machine and Big Data

Popis výsledku anglicky

This paper deals with sentiment analysis in text documents, especially text valence detection. The proposed solution is based on Support Vector Machines classifier. This classifier was trained with huge amount of data and complex word combinations were analysed. For this purpose distributed learning on 112 processors was used. Datasets used for training and testing were automatically obtained from real user feedback on products from different web pages (and different product segments). The proposed solution has been evaluated with different languages – English, German, Czech and Spanish. This paper improves accuracy achieved with the Big Data approach about 11%. The best accuracy achieved in this work was 95.31% for recognition of positive and negative text valence. The described learning is fully automatic, can be applied to any language and no complicated preprocessing is needed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016

ISBN

978-1-5090-1287-9

ISSN

—

e-ISSN

—

Počet stran výsledku

3

Strana od-do

543-545

Název nakladatele

Neuveden

Místo vydání

Vídeň

Místo konání akce

Vídeň

Datum konání akce

27. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390164000118