Sentiment analysis is an automated technique for extracting opinions, emotions, and sentiments from text and records internet-based attitudes and feelings. Individuals offer their perspectives on various topics through blog entries, comments, reviews, and tweets. Opinion mining and sentiment analysis can be used to monitor places, products, and brands and then determine if they are viewed positively or negatively. . Classification and clustering techniques are generally used for sentiment analysis. But, clustering-based techniques are compelling for sentiment analysis from the text. Although their results are subject to alteration depending on the data pre-processing method used or the terms weighting approach used, they have a significant advantage over supervised learning methods. Furthermore, clustering-based techniques can produce satisfactory results without needing organized data, linguistic knowledge, or training time. This paper will review recent works in sentiment analysis techniques and places' clustering techniques based on sentiment analysis in aim to cluster places based on safety measures during corona time for better user satisfaction.