Enhancing Accuracy in Social Media Sentiment Analysis through Comparative Studies using Machine Learning Techniques

In the general scope of social media analytics, sentiment analysis is one of the most significant tools that can be employed to elucidate useful information from a vast amount of textual data. However, one of the primary problems that still persist with sentiment analysis is its accuracy because it is hard to understand precisely what a person meant on the vastness of the Web. In this research, these machine learning algorithms such as Naive Bayes (NB), Support Vector Machines and others are compared to determine its efficiency in enhancing the rate of accuracy in sentiment analysis across social media. Through data collection from various social media sources, which are preceded by stringent pre-processing techniques such as text normalization and feature extraction the performance of each model is evaluated. However, the findings present significant disparities in these models’ accuracies, illustrating their best conditions of operation. This research helps to fill the gap in literature by offering a more subtle idea of strengths and weaknesses of every machine learning methodology when used for sentiment analysis applications, which is useful information on how researchers as well as practitioners can improve analytical accuracy within social media context.

I. Introduction

Social media has seen tremendous growth in size and importance, developing into an integral component of the normal life. It provides an opportunity for people to share their ideas and beliefs, thus forming a database of information that can be used in research. With the help of Social Media Analytics (SMA) and Sentiment Analysis (SA), users’ opinions can be quantified as well as qualitatively measured [1]. This section brings the notions of SMA and SA. Social media is referred to as web-based applications that are used for the easy creation, consumption and sharing of user generated content. Web 2.0 has changed the world of communication forever since it allowed people to connect via online platforms and social networks [2, 3]. These platforms are used by businesses to promote personal opinions, products and services. Every day, the number of social media users increases and currently stands at 3.80 billion digital participants worldwide as of January 2023. Content posted on social media is a wide range of forms like text, videos, pictures and also music [4, 5]. This type has created social media a potent instrument for getting and disseminating information throughout diverse areas of entertainment, business, science, politics as well as disaster management. One of the main reasons for social media’s success is its cost-efficiency in broadcasting and receiving public messages, which has led to an increase in user engagement resulting into a massive accumulation of data that comes from text pictures videos audio geolocation information. Social media data can be divided into unstructured and structured types, including textual information on one hand, user relations as another type [6, 7]. Social media usage has grown exponentially and enabled new fields of research, making it possible to explore social data in order to reveal what trends are current, public opinions or other types of informatio that would take surveys or focus groups on traditional basis [8, 9]. Such analysis is comparable to qualitative research and hence social media becomes a central element in computational social science studies. In this case, quantitative computational methods such as statistics, machine learning with data mining and simulation modeling are used to study problems. Analytics is the foundation of any marketing plan, and in particular it becomes the most critical component when we talk about digital channel because of its vast ecosystem where platforms, advertising and promotion need to be meticulously measured [10]. The figure.1. This provides a flowchart that depicts how the methods unfold over time and their interrelations in sentiment analysis with regards to social media.