  The development of emotion detection technology has emerged as a highly
valuable possibility in the corporate sector due to the nearly limitless uses
of this new discipline, particularly with the unceasing propagation of social
data. In recent years, the electronic marketplace has witnessed the
establishment of a large number of start-up businesses with an almost sole
focus on building new commercial and open-source tools and APIs for emotion
detection and recognition. Yet, these tools and APIs must be continuously
reviewed and evaluated, and their performances should be reported and
discussed. There is a lack of research to empirically compare current emotion
detection technologies in terms of the results obtained from each model using
the same textual dataset. Also, there is a lack of comparative studies that
apply benchmark comparison to social data. This study compares eight
technologies; IBM Watson NLU, ParallelDots, Symanto-Ekman, Crystalfeel, Text to
Emotion, Senpy, Textprobe, and NLP Cloud. The comparison was undertaken using
two different datasets. The emotions from the chosen datasets were then derived
using the incorporated APIs. The performance of these APIs was assessed using
the aggregated scores that they delivered as well as the theoretically proven
evaluation metrics such as the micro-average of accuracy, classification error,
precision, recall, and f1-score. Lastly, the assessment of these APIs
incorporating the evaluation measures is reported and discussed.
