Social media and retail sectors have become vital for consumer insights and analysis and this is commonly referred to as sentiment analysis. In the social media context, sentiment analysis refers to the process of identifying and categorizing the subjective content in social media posts including tweets, comments and posts to measure the sentiment on products, brands or events. By using methods such as NLP and machine learning, the sentiments can be categorised as positive, negative, or neutral where such results can be greatly beneficial to businesses in assisting them make sound decisions. In retail, sentiment analysis goes beyond social media by including such sources as customers’ reviews, feedback forms, forums, etc. with the purpose of revealing trends, recognizing customers’ satisfaction level, and finding out weak points of certain products or services. The combination of sentiment analysis with other analytical tools such as data mining and predictive analytics makes this tool useful in determining consumer’s behavior and developing relevant marketing strategies. Difficulties like sarcasm, irony, and language differences require more sophisticated NLP models that can handle contextual and sentiment analysis. However, issues of privacy and ethics in relation to the gathering and analysis of data are vital in determining responsible practices in sentiment analysis. In spite of these challenges, the use of sentiment analysis is on the rise, fuelled by the rising volumes of digital data and the need for organizations to monitor customers’ sentiments as they occur in real-time. Due to progressive technical development and constantly enlarging datasets, sentiment analysis is expected to impose an even more significant impact on the state and specificity of marketing and customer-orientation in the domains of social media and retail.