Sentimental Analysis of Product Reviews with Combined CNN-LSTM Model using Deep Learning

Automated textual data sentiment analysis is the main focus of sentiment analysis, a crucial area within NLP. This abstract gives a succinct review of the state of sentiment analysis today, focusing on methodology, difficulties, and prospects. Sentiment analysis has progressed significantly in recent times because of the explosion of user-generated material on digital platforms. Deep learning algorithms in particular have made it possible to extract sentiment from text, enabling insights into public sentiment across a variety of fields. Despite this, context dependence, sarcasm detection, and the requirement for large, domain-specific datasets present difficulties for sentiment analysis. The importance of ethical issues, particularly bias and privacy concerns has increased. This paper analyzes the uses of sentiment analysis in marketing, politics, and healthcare while charting the development of sentiment analysis methodologies.