Detail publikace

Genetic Optimization of Big Data Sentiment Analysis

POVODA, L. BURGET, R. DUTTA, M. K. SENGAR, N.

Originální název

Genetic Optimization of Big Data Sentiment Analysis

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

artificial intelligence; big data; data mining; opinion mining; sentiment analysis; text mining; text valence classification

Autoři

POVODA, L.; BURGET, R.; DUTTA, M. K.; SENGAR, N.

Vydáno

2. 2. 2017

ISBN

978-1-5090-2796-5

Kniha

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

Strany od

141

Strany do

144

Strany počet

4

URL

https://ieeexplore.ieee.org/document/8049932

BibTex

@inproceedings{BUT133480,
  author="Lukáš {Povoda} and Radim {Burget} and Malay Kishore {Dutta} and Namita {Sengar}",
  title="Genetic Optimization of Big Data Sentiment Analysis",
  booktitle="2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)",
  year="2017",
  pages="141--144",
  doi="10.1109/SPIN.2017.8049932",
  isbn="978-1-5090-2796-5",
  url="https://ieeexplore.ieee.org/document/8049932"
}