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Additionally, it was a key technology that contributed to the Fourth Industrial Revolution (IR 4.0). The subset of AI known as emotion recognition systems facilitates communication between IR 4.0 and IR 5.0. Nowadays users of social media, digital marketing, and e-commerce sites are increasing day by day resulting in massive amounts of unstructured data. Medical, marketing, public safety, education, human resources, business, and other industries also use the emotion recognition system widely. Hence it provides a large amount of textual data to extract the emotions from them. The paper presents a systematic literature review of the existing literature published between 2013 to 2023 in text-based emotion detection. This review scrupulously summarized 330 research papers from different conferences, journals, workshops, and dissertations. This paper explores different approaches, methods, different deep learning models, key aspects, description of datasets, evaluation techniques, Future prospects of deep learning, challenges in existing studies and presents limitations and practical implications.<\/jats:p>","DOI":"10.1007\/s10462-024-10831-1","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T12:02:17Z","timestamp":1720699337000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["A review on emotion detection by using deep learning techniques"],"prefix":"10.1007","volume":"57","author":[{"given":"Tulika","family":"Chutia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nomi","family":"Baruah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"issue":"9","key":"10831_CR1","first-page":"6652","volume":"34","author":"MM Abdelgwad","year":"2022","unstructured":"Abdelgwad MM, Soliman THA, Taloba AI, Farghaly MF (2022) Arabic aspect based sentiment analysis using bidirectional gru based models. 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