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SummarySentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity. Moreover, big data is produced via mobile networks and social media. Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment. Accordingly, performing sentiment analysis on big data can be helpful for a business to take useful commercial insights from text‐oriented content. However, there are very few comprehensive investigations and profound argument in this context. The goal of this paper is to provide a comprehensive and systematic investigation of the state‐of‐the‐art techniques and highlight the directions for future research. In this paper, we used systematic literature review method and in the first step, we obtained 15 351 articles; then, based on different filters, 48 related articles were attained. We have selected 23 articles based on the year of publication, the relevance of the journal, the completeness of the text, the nonrepeatability of the title, and the page number. Also, we have categorized big data and sentiment analysis into two classifications: centralized and distributed platforms. Furthermore, the disadvantages and advantages of the investigated techniques are studied and their key issues are emphasized. Consequently, this study shows that a better analysis of textual big data in terms of sentiment increases efficiency, flexibility, and intelligence. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better handling of big data in the field of sentiment analysis. This study sheds some new light on using sentiment analysis and big data for public opinion estimation and prediction.

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