Understanding Readers: Conducting Sentiment Analysis of Instagram Captions

Ming Zhan; Tu Ruibo; Yu Qin

doi:10.1145/3297156.3297270

Understanding Readers: Conducting Sentiment Analysis of Instagram Captions

Ming Zhan, Tu Ruibo, Yu Qin

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

8 Citations (Scopus)

Abstract

The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services.

Original language	Undefined/Unknown
Title of host publication	CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence
Publisher	ACM
Pages	33–40
ISBN (Print)	978-1-4503-6606-9
DOIs	https://doi.org/10.1145/3297156.3297270
Publication status	Published - 2018
MoE publication type	A4 Article in a conference publication
Event	conference - 2nd International Conference on Computer Science and Artificial Intelligence Duration: 1 Jan 2018 → …

Conference

Conference	conference
Period	01/01/18 → …

Access to Document

10.1145/3297156.3297270

Cite this

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title = "Understanding Readers: Conducting Sentiment Analysis of Instagram Captions",

abstract = "The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services.",

author = "Ming Zhan and Tu Ruibo and Yu Qin",

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AU - Zhan, Ming

AU - Ruibo, Tu

AU - Qin, Yu

PY - 2018

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N2 - The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services.

AB - The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services.

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