In any industry be it product-based or service, asking for consumer feedback on the offered goods and services is a standard procedure. Reviews for the products and services come from various sources like online portals, and customer forums or can also come from social media platforms. The reviews for new products or services could be in tens of thousands or even millions. Understanding the sentiments and emotions in the reviews will help the business gauge the overall feedback and client satisfaction levels. The objective of this study is to identify sentiments and emotions in textual data using TextBlob and NRC emotion lexicons. For this study, data is collected from social media platforms. To identify the Sentiments and Emotions in textual data, various lexicons kinds are available. In this study, the National Research Council’s (NRC) emotion lexicons are utilized to identify and categorize emotions into the following happiness, fear, anger, surprise, trust, anticipation, disgust, and sadness. To score the sentiments TextBlob a common natural language processing (NLP) is used. TexBlob provides a score for each piece of text analyzed, ranging from −1 (most negative) to +1 (most positive), which determines the polarity of the text. Scores are also given for the text’s subjectivity/objectivity, which ranges from 0 to 1. By identifying the factor that determines customer sentiment and emotions, the business can make targeted improvements to its products and services. Using the NRC Emotion Lexicon as a model to analyze the sentiments, the accuracy of the model was found to be 0.75, and precision and recall were 0.76 and 0.72, respectively.