Publication detail

Genetic Optimization of Big Data Sentiment Analysis

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

Original Title

Genetic Optimization of Big Data Sentiment Analysis

English Title

Genetic Optimization of Big Data Sentiment Analysis

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

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

Released

02.02.2017

ISBN

978-1-5090-2796-5

Book

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

Pages from

141

Pages to

144

Pages count

4

BibTex


@inproceedings{BUT133480,
  author="Lukáš {Povoda} and Radim {Burget} and Malay Kishore {Dutta} and Namita {Sengar}",
  title="Genetic Optimization of Big Data Sentiment Analysis",
  annote="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.",
  booktitle="2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)",
  chapter="133480",
  howpublished="electronic, physical medium",
  year="2017",
  month="february",
  pages="141--144",
  type="conference paper"
}