A Large-Scale Implementation Using MapReduce-Based SVM for Tweets Sentiment Analysis

被引：0

作者：

Lijo, V. P. ^{[1
]}

Seetha, Hari ^{[2
]}

机构：

[1] Vellore Inst Technol, Vellore, Tamil Nadu, India

[2] VIT AP, Amaravati, Andhra Pradesh, India

来源：

INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019 | 2020年 / 1034卷

关键词：

Sentiment analysis; Polarity detection; Big data; ALGORITHM;

D O I：

10.1007/978-981-15-1084-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sentiment analysis is an interesting area of research due to the availability of sentiment data and opinion-oriented services. The efficiency and scalability of the sentiment analysis applications are important concerns as they expect accurate results in short period of time by processing a large amount of data. An efficient and scalable polarity detection method is proposed in this paper. The sequential minimal optimization with Map Reduce (SMOMR) is used to achieve enhanced efficiency as well as scalability. The experiment results reveal that this method outperforms many existing methods.

引用

页码：541 / 549

页数：9

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