a comparison study on sentiment analysis and emotion detection in marketing analytics based on ai approach

Sentiment analysis is a method to determine the sentiment or opinion expressed in text data, enabling companies to understand customer feedback, brand perception, and make data-driven decisions. there exist various approaches to handling emotions, one of which involves the utilization of deep learning algorithms and machine learning techniques.this study explores sentiment analysis using machine learning and deep learning algorithms.we compare different algorithms, especially svm, which is a powerful method that can deal with complex and high-dimensional data, with other methods such as naive bayes, decision tree, and random forest .according to the paper, all of the techniques utilized in the study have proven to yield impressive accuracy and f1 scores exceeding 75%. combining pv-dbow or pv-dm with svm or logistic regression has been found to yield the best outcomes, achieving an accuracy of approximately 87% and an f1 score of 81%. the paper also highlights that pv-dbow, in conjunction with logistic regression, classifies certain data differently, possibly due to an imbalance in the data that favors negative sentiment. to enhance performance and outcomes, the paper proposes several future research directions.