Real-Time Sentiment Analysis of Social Media Content for Brand Improvement and Topic Tracking

DOI

10.2991/978-94-6463-250-7_6How to use a DOI?

Keywords

Social media; Naive Bayes Classifier; Feature Extraction; NLP; Sentiment Analysis

Abstract

This research paper focuses on real-time sentiment analysis of social media content for brand improvement and topic tracking. With the advent of social media, customers can easily express their opinions and emotions about a brand or product. As a result, businesses need to monitor social media channels to understand their customers’ sentiments and to make informed decisions that can improve their brand’s reputation. This study aims to create a sentiment analysis system that can quickly and accurately determine the sentiment of social media content in real-time. The system classifies the sentiment of the text as good, negative, or neutral using natural language processing algorithms. Additionally, the research explores the use of topic modelling techniques to track trending topics and identify issues that may be affecting the brand’s reputation. The system is tested on a large dataset of tweets related to various brands and topics. The outcomes show that the suggested method is capable of precisely determining the sentiment of social media content and monitoring trending topics. The research findings can provide valuable insights to businesses for brand improvement and to take timely actions to address any potential issues.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Atimabh Barunaha
AU  - Motati Ram Prakash
AU  - R. Naresh
PY  - 2023
DA  - 2023/10/17
TI  - Real-Time Sentiment Analysis of Social Media Content for Brand Improvement and Topic Tracking
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 26
EP  - 31
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_6
DO  - 10.2991/978-94-6463-250-7_6
ID  - Barunaha2023
ER  -