## 2 Materials and Methods
In a study [12] on Logistic Regression with Count Vectorizer (LRCV), a combined approach was implemented by the authors on product reviews for sentiment analysis to convert text into numerical vectors, which further feed as input into a machine learning model. The test was conducted on benchmark datasets, producing better results across various performance metrics. To conclude, from the extensive related work and the literature survey, we observed significant contributions made by many researchers on TSA. The researchers have implemented different machine learning and deep learning algorithms on a wide range of datasets. However, the most significant research gap noticed is the absence of a proper dataset specifically tailored for TSA and an absence of comparisons among machine learning algorithms for sentiment analysis and classification. In this regard, our proposed work makes a unique contribution for TSA in terms of the vectorization techniques, preprocessing steps used, algorithms implemented for sentiment classification, and finally the utilization of statistical and analytical approaches to interpret the results. To address and understand Twitter Sentiment Analysis, various research questions are addressed as follows. Research Questions and Inferences in TSAIn the context of TSA, the research questions and inferences focus on understanding the sentiment (positive, negative) expressed in tweets. The following are some research questions and potential conclusions related to TSA:  
RQ1: What factors influence Twitter sentiment polarization during public events? A variety of factors can influence Twitter sentiment polarization during public events. These factors can contribute to the division of opinions, resulting in a strong separation between positive and negative sentiments. The list includes event controversy, political or ideological affiliations, media coverage and framing, inflammatory tone and language, misinformation, etc. RQ2: Why has Twitter been chosen as the target platform for the study of sentiment analysis? Twitter has been chosen as a popular platform because of its increasing number of active users over time, and the platform can make it brief and simple to collect vast quantities of passionate and real-time text data. Furthermore, it often includes hash tags and keywords to categorize and highlight the tweets which give researchers insights, allowing a deeper understanding of sentiment dynamics. RQ3: Can sentiment analysis on Twitter provide more timely and accurate insights compared to traditional methods? TSA can provide more timely insights than traditional methods of tracking public opinion, thus allowing for a quicker identification of emerging trends and events; on the other hand, traditional methods like surveys might take more time to design, conduct, and analyze which could lead to a delay in capturing the current sentiment. RQ4: Could sentiment analysis benefit from integrating visual content (images and videos) alongside textual analysis within tweets? Yes, combining the textual analysis of tweets with visual content could be helpful for sentiment analysis. The accuracy and depth of sentiment analysis results can be improved by using visual content, which can give context and clues that may not be available in the text alone.