Real-Time Analysis of Sentiment in Blog Posts Problem Statement

Problem statement

Understanding the sentiment expressed in blog posts is crucial for content
creators, marketers, and readers. Real-time analysis of sentiment can provide
valuable insights into the emotional tone of blog posts, helping to gauge
audience reactions.

Abstract

This project focuses on implementing a real-time sentiment analysis system for
blog posts. Using natural language processing and machine learning, the system
will analyze the sentiment expressed in blog content as it is published, providing
instant insights into the emotional tone of the posts.

Outcome

A real-time sentiment analysis tool for blog posts that enables content creators
and marketers to understand audience reactions, improve content strategies, and
engage with readers more effectively.

Reference

For decades, researchers have experimented with the possibility that machines can equal human
linguistic capabilities. Recently, advances in the field of natural language processing (NLP) as well as a substantial increase in available naturally occurring linguistic data on social media platforms have made more advanced methodologies such as sentiment analysis (SA) gain substantial momentum on contemporary applications. This document compiles what the authors consider to be some of the most important concepts related to SA, as well as techniques and processes necessary for the various stages of its implementation. Furthermore, specific applications related to the extraction and classification of social media data using novel SA techniques are presented and quantified, with an emphasis on those used for the identification of mental health degradation during the COVID-19 pandemic. Finally, the authors present several conclusions highlighting the most prominent benefits and drawbacks of the methods discussed, followed by a brief discussion of possible future applications of certain methods of interest.

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