Expansion of Internet-based applications has made e-commerce a natural emphasis for businesses, typically at the expense of traditional brick and mortar business platforms. This has led to an enormous increase in the amount of “text” that is produced by user comments and reviews. This data is useful for business owners who are looking to measure the societal influence of their products, brand or service and use that information to make good decisions. A sub-field of machine learning (ML), sentiment analysis classifies content tone from the author as positive, neutral or negative giving insights on natural language processing which will allow companies to gauge customer or potential client sentiments. Sentiment analysis is harder with shorter sentences, as they oftentimes contain less relevant information for the task. There are numerous challenges in performing accurate sentiment analysis and evaluation. Sentiment analysis refers to the use of natural language processing, text mining and computational linguistics to identify and extract subjective information in textual content. In this paper, a wide-ranging survey on methodologies applied in sentiment analysis is presented and the latest developments achieved after 2020 are also reviewed.