Juncture of Text Preprocessing Techniques & Extracting Sentiment Analyzing of Micro-Blog Based on Machine Learning Algorithms

Sentiment analysis finds applications in various fields, including marketing, social blog monitoring, public opinion, and customer feedback analysis. Sentiment analysis is the technique of extracting and infers subjective information from content using computational methods, NLP, and phrase analysis. The objective of sentiment analysis’s is to analyze a emotional tone, posture, or opinion identify if a text, such as reviews, social media postings, surveys, or news articles, and classify them as positive, negative, or neutral. Preprocessing methods are used to enhance a quality and relevance of data to be able to optimize the efficiency for any analytical or machine learning (ML) model. These techniques involve various activities such as data cleaning, normalization, transformation, feature engineering, and data integration. In this paper, describe the phase of preprocessing methods transforms raw data into a format that will be effectively processed in machine learning model. In proposed work, we used four ML model (Logistic regression (LR), Decision tree (DT) models, Multinomial Naive Bayes (MNB) and support Vector machine(SVM) model) for train and test the Twitter Sentiment(TS) Review Dataset. It improves the accuracy, consistency, and reliability of data.