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Sentiment Analysis of Social Media: A Case Study on Big Tech Layoffs

Authors:
Mehdi Mekni
Ahmed Muntasir Hossain

Keywords: Sentiment Analysis, Natural Language Processing, Social Media Platforms.

Abstract:
Digital reputation management systems are essential for maintaining and improving online reputations. However, current systems like Online Social Network Interactions face critical issues, such as limited effectiveness, high costs, and inaccuracy. Sentiment analysis, a natural language processing technique, can enhance digital reputation management by extracting opinions, emotions, and attitudes from textual data. We propose developing Sentiment Analysis of Social Media, an open-source, multi-channel, multi-engine sentiment analysis software. SASM collects data from Twitter, Reddit, and Tumblr, filtering and analyzing trends using Microsoft Text Analytics, IBM Watson Natural Language Understanding, and Google Cloud Natural Language API. A case study on Google, Amazon, and Microsoft will validate the system and evaluate the performance of the three engines. SASM offers a unique approach by providing reliable sentiment analysis, leveraging multiple engines, and sourcing diverse social media content, enabling companies to manage their digital reputation effectively and affordably.

Pages: 1 to 9

Copyright: Copyright (c) IARIA, 2024

Publication date: November 3, 2024

Published in: conference

ISBN: 978-1-68558-212-8

Location: Nice, France

Dates: from November 3, 2024 to November 7, 2024