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

共 50 条

[1] Comparison of SVM and Naive Bayes for Sentiment Classification using BERT data
Rana, Shivani
Kanji, Rakesh
Jain, Shruti
[J]. 2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT), 2022,
[2] Sentiment Analysis using Naive Bayes and Complement Naive Bayes Classifier Algorithms on Hadoop Framework
Seref, Berna
Bostanci, Erkan
[J]. 2018 2ND INTERNATIONAL SYMPOSIUM ON MULTIDISCIPLINARY STUDIES AND INNOVATIVE TECHNOLOGIES (ISMSIT), 2018, : 555 - 561
[3] Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset
Rahat, Abdul Mohaimin
Kahir, Abdul
Masum, Abu Kaisar Mohammad
[J]. PROCEEDINGS OF THE 2019 8TH INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART-2019), 2019, : 266 - 270
[4] Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media
Permana, Fahmi Candra
Rosmansyah, Yusep
Abdullah, Atje Setiawan
[J]. ASIAN MATHEMATICAL CONFERENCE 2016 (AMC 2016), 2017, 893
[5] Naive Bayes and SVM based NIDS
Dixit, Mrudul
Moholkar, Ankita
Limaye, Sagarika
Limaye, Devashree
[J]. PROCEEDINGS OF THE 2018 3RD INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2018), 2018, : 527 - 532
[6] Social Media Sentiment Analysis Using K-Means and Naive Bayes Algorithm
Zul, Muhammad Ihsan
Yulia, Feoni
Nurmalasari, Dini
[J]. 2018 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS (ICON EEI): TOWARD THE MOST EFFICIENT WAY OF MAKING AND DEALING WITH FUTURE ELECTRICAL POWER SYSTEM AND BIG DATA ANALYSIS, 2018, : 24 - 29
[7] Comparative Analysis of Sentiment Orientation Using SVM and Naive Bayes Techniques
Rana, Shweta
Singh, Archana
[J]. PROCEEDINGS ON 2016 2ND INTERNATIONAL CONFERENCE ON NEXT GENERATION COMPUTING TECHNOLOGIES (NGCT), 2016, : 106 - 111
[8] CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media
Patil, Harshali P.
Atique, Mohammad
[J]. BIG DATA, 2020, 8 (02) : 107 - 124
[9] Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM
Hasanli, Huseyn
Rustamov, Samir
[J]. 2019 IEEE 13TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT 2019), 2019, : 124 - 130
[10] Effect of Training Set Size on SVM and Naive Bayes for Twitter Sentiment Analysis
Abdelwahab, Omar
Bahgat, Mohamed
Lowrance, Christopher J.
Elmaghraby, Adel
[J]. 2015 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2015, : 46 - 51

← 1 2 3 4 5 →