## 1 Introduction  
In today's digital landscape, user feedback and reviews on social media platforms like Twitter, Facebook, and Instagram hold significant value for organizations and play a predominant role aiming to enhance services and products and manage the whole performance efficiently. People can openly express their thoughts, ideas, and views as short messages called tweets on many micro-blogging platforms on social networks and web forums [1]. Organizations frequently employ sentiment analysis or opinion mining techniques to extract meaningful information from the unstructured user inputs [2]. Sentiment analysis involves assessing emotions, opinions, and attitudes expressed within text data. Twitter serves as a rich source for the analysis due to its real-time nature and the vast amount of user-generated content [3]. Notably, popular Twitter users like [PERSON] receive over 300,000, an excessive volume of tweets every day. Similarly, accounts like Xbox Support, which has over 400,000 followers, face the daunting task of managing and responding to more than 1.5 million daily tweets. During notable events like the 2024 IPL match, Twitter experienced an even more significant surge, with over 750 million tweets related to the tournament sent during that time. Handling such massive datasets poses a considerable workload for any organization. The applications offered by TSA exhibit inadequate performance capabilities. According to a literature assessment, the accuracy metrics for sentiment analysis generally range from 40% to 80%. There is a need for more improved and precise TSA tools for firms in reviewing client feedback and analyzing sentiments.  
Tweets use a wide-ranging, diverse, and ever-changing vocabulary that includes slang, acronyms, and emojs. Furthermore, tweets offer only a restricted number of and shorter terms for sentiment analysis. This leads to tweet features having sparse representation, a common issue found with standard feature sets, which typically leads to the suboptimal performance of sentiment analysis algorithms.  
Some of the key contributions of this article are the following points:  
* Introduction of a Twitter-specific sophisticated lexicon set to avoid the ambiguity of sentiments at a sentence level.
* Analysis of target class determination and domain adaptability using classifiers.
* Implementation of the MVT (Majority Voting Technique) with a Random Forest classifier for an improvement in accuracy and other performance measures in sentiment analysis tasks.  
The sequence of steps involved in the model is depicted in Figure 1, which ultimately led the classification of tweets as either positive or negative.  
The subsequent sections of this article are structured as follows: Section 2 discusses related work, examining existing research in the domain. Section 3 introduces the proposed framework, providing an in-depth overview of its structure and components. Section 4 outlines the vectorization techniques employed in this study. Section 5 illustrates various machine learning models. Section 6 furnishes dataset descriptions and word cloud results. Section 7 is the Results and Discussion. This study eventually ends with an outcome and future plans for more work.