Analyzing Sentiment Level of Social Media Data Based on SVM and Naive Bayes Algorithms

被引：0

作者：

Naing, Hsu Wai ^{[1
]}

Thwe, Phyu ^{[1
]}

Mon, Aye Chan ^{[1
]}

Naw, Naw ^{[1
]}

机构：

[1] Univ Technol Yatanarpon Cyber City, Dept Informat Sci, Pyin Oo Lwin, Myanmar

来源：

BIG DATA ANALYSIS AND DEEP LEARNING APPLICATIONS | 2019年 / 744卷

关键词：

Opinion mining; Sentiment analysis; Twitter; Support vector machine (SVM); Naive bayes (NB); Text classification;

D O I：

10.1007/978-981-13-0869-7_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Social media is a popular network through which users can share their reviews about various topics, news, products etc. People use internet to access or update reviews so it is necessary to express opinion. Twitter is a hugely valuable resource from which insights can be extracted by using text mining tools like sentiment analysis. Sentiment analysis is the task of identifying opinion from reviews. The system performs classification by combining Naive Bayes (NB) and Support Vector Machine (SVM). The system is intended to measure the impact of ASEAN citizens' social media based on their usage behavior. The system is developed for analyzing National Educational Rate, Business Rate and Crime Rate occurred in Malaysia, Singapore, Vietnam and our country, Myanmar. The system compares the performance of these two classifiers in accuracy, precision and recall.

引用

页码：68 / 76

页数：9

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