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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The importance of big data in machine learning cannot be overemphasized in recent times. Through the evolution of big data, most scientific technologies that relied heavily on enormous data in solving complex issues in human lives gained grounds; machine learning is an instance of these technologies. Various machine learning models that yield groundbreaking throughputs with high efficiency rates in predicting, detecting, classifying, discovering and acquiring in-depth knowledge about events that would otherwise be very difficult to ascertain have been made possible due to big data. Although big data has undoubtedly helped in the field of machine learning research ,over the years, its mode of acquisition has posed great challenge in industries,education and other agencies that obtained them for various purposes. This is because these large quantities of data cannot be stored on personal computers with limited storage capabicity but required the use of high storage capacity servers for effective storage. These servers may be owned by a group of companies or individuals who had the singular priviledge to modify the data in their possession as and when deemed relevant thus the creation of a centralized data storage environment. These were mostly refered to as the Third Parties (TP) in the data acquisition process. For the services they rendered, these trusted parties priced data in their possession expensively. The adverse effect is a limitation on various researches that could help solve a number of problems in human lives. It is worth mentioning that the security of these data being purchased expensively cannot be even assured limiting various researches that thrive on secured data. In order to curb these occurrences and have better machine learning models, the incorporation of Blockchain Technology databases into machine learning. This paper discusses the concept of big data, Machine Learning and Blockchains. It further discusses how Big data has impacted the Machine learning Community, the significance of Machine Learning and how the BlockChain Technology could be used similarly impact the Machine Learning Community. The aim of this paper is to encourge further research in incoporating the BlockChain Technology into Machine Learning.

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The cover image is based on the Review Article Text based emotion detection: Advances, challenges, and opportunities by Francisca Adoma Acheampong et al., https://doi.org/10.1002/eng2.12189. image