Leveraging Machine Learning Techniques to Analyze Customer Reviews

Sentiment analysis, also known as opinion mining, is a powerful tool for extracting subjective information from huge data. Analysis is required to extract useful data from these sentiments which are useful to us. This task of grouping the reviews aims to distinguish a variety of opinion of clients. Interpreting human emotion is a composite chore that requires the skill to discern subtleties, detect sarcasm and irony, and reveal a high degree of sensitivity to circumstance and words adeptness. Challenges include absence of Direct Correspondences, understanding within cultural contexts, Idiomatic phrases, Emotional tones, and literary subtleties, tracking numerous channels etc. Diverse researchers carried out research work on analysis of sentiment using various machine learning algorithms such as binary classification, multiclass classification, multi-label classification, Support vector machine (SVM), Neural network, Clustering, Regression etc. This research focuses on applying sentiment analysis techniques to a twitter dataset to determine the underlying emotions or opinions expressed within it. With the growing importance of understanding social media sentiment, user feedback, and common opinion, this research emphasizes the need for an accurate and efficient sentiment analysis model. The findings highlight the potential of SVM as the optimal choice for large dataset sentiment analysis tasks even though it gives less results than other algorithm when working on large data, particularly when precision is crucial. Support Vector Machine has testing accuracy of 75.89% and Logistic Regression has testing accuracy of 77.79% and precision, recall and F1score values are more in training than testing because data is varied while testing. This relative analysis demonstrates the usefulness of machine learning techniques in sentiment categorization but also provides important guidance for researchers and practitioners seeking to set up consistent opinion analysis models.