Twitter sentiment analysis using a modified naïve bayes algorithm

Manav Masrani, Poornalatha Gou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Microblogging has emerged as a popular platform and a powerful communication tool among people nowadays. A clear majority of people share their opinions about various aspects of their lives online every day. Thus, microblogging websites offer rich sources of data in order to perform sentiment analysis and opinion mining. Because microblogging has emerged relatively recently there are only some research works which are devoted to this field. In this paper, the focus is on performing the task of sentiment analysis using Twitter which is one of the most popular microblogging platforms. Twitter is a very popular microblogging site where its users write status messages called tweets to express themselves. These status updates mostly express their opinions about various topics. The objective of this paper is to build a system that can classify these Twitter status updates as positive, negative, or neutral with respect to any query term thereby giving an idea about the overall sentiment of the people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments, marketers, or any organization who are researching public opinion.

Original languageEnglish
Title of host publicationInformation Systems Architecture and Technology
Subtitle of host publicationProceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017
PublisherSpringer Verlag
Pages171-181
Number of pages11
Volume655
ISBN (Print)9783319672199
DOIs
Publication statusPublished - 2018
Event38th International Conference on Information Systems Architecture and Technology, ISAT 2017 - Szklarska Poreba, Poland
Duration: 17-09-201719-09-2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume655
ISSN (Print)2194-5357

Conference

Conference38th International Conference on Information Systems Architecture and Technology, ISAT 2017
CountryPoland
CitySzklarska Poreba
Period17-09-1719-09-17

Fingerprint

Websites
Communication
Industry

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Masrani, M., & Gou, P. (2018). Twitter sentiment analysis using a modified naïve bayes algorithm. In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017 (Vol. 655, pp. 171-181). (Advances in Intelligent Systems and Computing; Vol. 655). Springer Verlag. https://doi.org/10.1007/978-3-319-67220-5_16
Masrani, Manav ; Gou, Poornalatha. / Twitter sentiment analysis using a modified naïve bayes algorithm. Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Vol. 655 Springer Verlag, 2018. pp. 171-181 (Advances in Intelligent Systems and Computing).
@inproceedings{574b3208ac92475d97afd8c890377815,
title = "Twitter sentiment analysis using a modified na{\"i}ve bayes algorithm",
abstract = "Microblogging has emerged as a popular platform and a powerful communication tool among people nowadays. A clear majority of people share their opinions about various aspects of their lives online every day. Thus, microblogging websites offer rich sources of data in order to perform sentiment analysis and opinion mining. Because microblogging has emerged relatively recently there are only some research works which are devoted to this field. In this paper, the focus is on performing the task of sentiment analysis using Twitter which is one of the most popular microblogging platforms. Twitter is a very popular microblogging site where its users write status messages called tweets to express themselves. These status updates mostly express their opinions about various topics. The objective of this paper is to build a system that can classify these Twitter status updates as positive, negative, or neutral with respect to any query term thereby giving an idea about the overall sentiment of the people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments, marketers, or any organization who are researching public opinion.",
author = "Manav Masrani and Poornalatha Gou",
year = "2018",
doi = "10.1007/978-3-319-67220-5_16",
language = "English",
isbn = "9783319672199",
volume = "655",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "171--181",
booktitle = "Information Systems Architecture and Technology",
address = "Germany",

}

Masrani, M & Gou, P 2018, Twitter sentiment analysis using a modified naïve bayes algorithm. in Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. vol. 655, Advances in Intelligent Systems and Computing, vol. 655, Springer Verlag, pp. 171-181, 38th International Conference on Information Systems Architecture and Technology, ISAT 2017, Szklarska Poreba, Poland, 17-09-17. https://doi.org/10.1007/978-3-319-67220-5_16

Twitter sentiment analysis using a modified naïve bayes algorithm. / Masrani, Manav; Gou, Poornalatha.

Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Vol. 655 Springer Verlag, 2018. p. 171-181 (Advances in Intelligent Systems and Computing; Vol. 655).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Twitter sentiment analysis using a modified naïve bayes algorithm

AU - Masrani, Manav

AU - Gou, Poornalatha

PY - 2018

Y1 - 2018

N2 - Microblogging has emerged as a popular platform and a powerful communication tool among people nowadays. A clear majority of people share their opinions about various aspects of their lives online every day. Thus, microblogging websites offer rich sources of data in order to perform sentiment analysis and opinion mining. Because microblogging has emerged relatively recently there are only some research works which are devoted to this field. In this paper, the focus is on performing the task of sentiment analysis using Twitter which is one of the most popular microblogging platforms. Twitter is a very popular microblogging site where its users write status messages called tweets to express themselves. These status updates mostly express their opinions about various topics. The objective of this paper is to build a system that can classify these Twitter status updates as positive, negative, or neutral with respect to any query term thereby giving an idea about the overall sentiment of the people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments, marketers, or any organization who are researching public opinion.

AB - Microblogging has emerged as a popular platform and a powerful communication tool among people nowadays. A clear majority of people share their opinions about various aspects of their lives online every day. Thus, microblogging websites offer rich sources of data in order to perform sentiment analysis and opinion mining. Because microblogging has emerged relatively recently there are only some research works which are devoted to this field. In this paper, the focus is on performing the task of sentiment analysis using Twitter which is one of the most popular microblogging platforms. Twitter is a very popular microblogging site where its users write status messages called tweets to express themselves. These status updates mostly express their opinions about various topics. The objective of this paper is to build a system that can classify these Twitter status updates as positive, negative, or neutral with respect to any query term thereby giving an idea about the overall sentiment of the people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments, marketers, or any organization who are researching public opinion.

UR - http://www.scopus.com/inward/record.url?scp=85029521435&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029521435&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-67220-5_16

DO - 10.1007/978-3-319-67220-5_16

M3 - Conference contribution

SN - 9783319672199

VL - 655

T3 - Advances in Intelligent Systems and Computing

SP - 171

EP - 181

BT - Information Systems Architecture and Technology

PB - Springer Verlag

ER -

Masrani M, Gou P. Twitter sentiment analysis using a modified naïve bayes algorithm. In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Vol. 655. Springer Verlag. 2018. p. 171-181. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-67220-5_16