Genetic Optimization of Big Data Sentiment Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F17%3APU122812" target="_blank" >RIV/00216305:26220/17:PU122812 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8049932" target="_blank" >https://ieeexplore.ieee.org/document/8049932</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SPIN.2017.8049932" target="_blank" >10.1109/SPIN.2017.8049932</a>

Result language

angličtina

Original language name

Genetic Optimization of Big Data Sentiment Analysis

Original language description

This paper deals with opinion mining from unstructured textual documents. The proposed method focuses on approach with minimum preliminary requirements about the knowledge of the analysed language and thus it can be deployed to any language. The proposed method builds on artificial intelligence, which consists of Support Vector Machines classifier, Big Data analysis and genetic algorithm optimization. To make the optimization feasible together with big data approach we have proposed GA operators, which significantly accelerate conversion to the accurate solutions. In this work we outperformed the traditional approaches (which use language dependent text preprocessing) for text valence classification with the highest achieved accuracy 90.09 %. The data set for validation was Czech texts.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2017

Confidentiality

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

Article name in the collection

2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)

ISBN

978-1-5090-2796-5

ISSN

—

e-ISSN

—

Number of pages

4

Pages from-to

141-144

Publisher name

Neuveden

Place of publication

neuveden

Event location

Dept. of ECE, ASET, Amity University, Noida Sec-

Event date

Feb 2, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000426076800029