An Improved SVM for Book Review Sentiment Polarity Analysis

DOI

10.2991/tlicsc-18.2018.23How to use a DOI?

Keywords

Support vector machine, Sentiment polarity analysis, Book review.

Abstract

In the internet age, whether a book has the value of reading, online comments play an important role. The data set in this paper is 4,000 comments obtained by the web crawler in Douban Reading. Based on the improved support vector machine (SVM) algorithm, a sentiment analysis has been given to these comments. The experimental results show that the improved SVM algorithm has a good effect on the rate and accuracy of sentiment polarity analysis of book reviews.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xinxin Guan
AU  - Yeli Li
AU  - Hechen Gong
AU  - Huayan Sun
AU  - Chufeng Zhou
PY  - 2018/12
DA  - 2018/12
TI  - An Improved SVM for Book Review Sentiment Polarity Analysis
BT  - Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)
PB  - Atlantis Press
SP  - 139
EP  - 143
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.23
DO  - 10.2991/tlicsc-18.2018.23
ID  - Guan2018/12
ER  -