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POVODA, L. BURGET, R. DUTTA, M.
Original Title
Sentiment Analysis Based on Support Vector Machine and Big Data
Type
conference paper
Language
English
Original Abstract
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.
Keywords
text valence, classification, emotion recognition, text mining, cluster computing
Authors
POVODA, L.; BURGET, R.; DUTTA, M.
Released
27. 6. 2016
Location
Vídeň
ISBN
978-1-5090-1287-9
Book
Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016
Pages from
543
Pages to
545
Pages count
3
URL
https://ieeexplore.ieee.org/document/7760939
BibTex
@inproceedings{BUT127869, author="Lukáš {Povoda} and Radim {Burget} and Malay Kishore {Dutta}", title="Sentiment Analysis Based on Support Vector Machine and Big Data", booktitle="Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016", year="2016", pages="543--545", address="Vídeň", doi="10.1109/TSP.2016.7760939", isbn="978-1-5090-1287-9", url="https://ieeexplore.ieee.org/document/7760939" }