Sentiment Polarity Identification of Social Media content using Artificial Neural Networks

K. Victor Rajan; Brittney Jackson

doi:10.34257/GJCSTDVOL22IS1PG1

Sentiment Polarity Identification of Social Media content using Artificial Neural Networks

K. Victor Rajan * §

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Brittney Jackson

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§ Atlantic International University

Sentiment Polarity Identification of Social Media content using Artificial Neural Networks

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CSTP7653

DOI
10.34257/GJCSTDVOL22IS1PG1

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Abstract

Sentiment of people about consumer goods and government policies for decision making is normally collected through feedback forms, surveys etc. The social network sites and micro blogging sites are considered a very good source of information nowadays because people share and discuss their opinions about a certain topic freely. With the increased use of technology and social media, people proactively express their opinion through social media sites like Twitter, Facebook, Instagram etc. A social media sentiment analysis can help companies to understand how people feel about their products. On the other hand, extracting the sentiment from social media text is a challenging task due to the complexity of natural language processing of social media language. Often these messages reflect the emotion, opinion and sentiment of the public through a mix of text, image, emoticons etc. These statements are often called electronic Word of Mouth (eWOM) and are much prevalent in business and service industry to enable customers to share their point of view.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

K. Rajan, Brittney Jackson. 2026. "Sentiment Polarity Identification of Social Media content using Artificial Neural Networks". Global Journal of Computer Science and Technology, Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 22 (GJCST Volume 22 Issue D1).

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GJCST Volume 22 Issue D1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification F.1.1

Submission ReceivedAugust 9, 2021
Peer Review Double Blind
Handling Editor
Accepted September 8, 2021
Published September 18, 2021

Version of record

v1.2

Issue date

January 22, 2022

Language

English

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