With the continuous growth of web technology, there has been a significant increase in the volume of data available on the internet. This vast amount of data is generated by internet users who utilize online platforms for learning, exchanging ideas, and sharing opinions. Social networking sites such as Twitter, Facebook, and Google+ have gained immense popularity due to their ability to facilitate global discussions, enable expression of viewpoints, and allow individuals to post messages worldwide. Sentiment analysis, a subfield of text mining, focuses on the computational examination of people's opinions, attitudes, and emotions towards a particular subject or entity. This analysis, also known as opinion mining, aims to categorize articles based on their contributions to various sentiment analysis techniques. The objective of this article is to provide a comprehensive overview of sentiment analysis techniques and their related fields with concise explanations. In this study, sentiment analysis is applied to a dataset, which is then divided into positive and negative clusters based on the sentiments expressed. The paper introduces a novel hybrid algorithm for sentiment analysis, designed to enhance accuracy compared to previous methods. Overall, this research contributes to the understanding of sentiment analysis techniques, providing insights into their applications and proposing an improved algorithm for sentiment analysis.

Sentiment Analysis, Twitter Data, Sentiment Classification, Opinion Mining, Hybrid Approach, Novel Data Model algorithm.