Abstract
Using Slack for communication and teamwork is a common practice in the IT industry. It provides multiple Application Program Interfaces (APIs), which help developers significantly in writing a set of routines and protocols for software. IT companies conduct various sessions to educate, and to motivate their employees. The quality of those sessions can be improved by taking feedback and act accordingly. Slack APIs can be useful in taking such feedback. To simplify the feedback collection method, we came up with the ‘Slack Feedback Analyzer (SFbA)’, which collects feedback on session content as well as presenter’s presentation skills and the knowledge expertise on that topic. With the help of this application, we are presenting the most common keywords, audience focused on, and the number of positive, negative, and neutral feedback.
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Bobhate, R., Malhotra, J. (2021). Slack Feedback Analyzer (SFbA). In: Singh, V., Asari, V., Kumar, S., Patel, R. (eds) Computational Methods and Data Engineering. Advances in Intelligent Systems and Computing, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6876-3_30
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DOI: https://doi.org/10.1007/978-981-15-6876-3_30
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