Thanks Aliya for really eye-opening comments, it is so true that we (at least some, probably including me,) are motivated by the notion that we are cared, considered, and appreciated by our supervisors and peers.
That said, I think the feedback loop is very important when a company wants to gather sentiment analysis results.
If employees like me get the feedback and a company/supervisor tries to address below issues proactively and concretely, I may be positive for this:
a) overall sentiment of the company – how the company plans to react?
b) my own sentiment, probably this time and historical results – how supervisor felt for the results, how we can improve?
But thanks again, cannot agree with you more.
Incredibly interesting topics as well as commentary, thanks for sharing!
“ten cringe-worthy evaluation phrases” is super interesting to go through, and strongly reminded my of how difficult it is to objectively understand these comments without having biases…
It is also interesting to see the “power words” list, especially that “eager”, “good”, “wingman”, “top” are all in the bottom category indicators (kind of make sense… but I’m pretty sure that analytics can do much better job than human beings on analyzing these objectively in way more efficient manner).
Also wondering if there could be any way to incorporate this type of machine learning analysis to the consulting evaluation process. Tons of calibration work are needed and yet many complain about the outcomes. Since more and more consulting firms have stated focusing on DX as their client services, I hope these firms also apply people analytics in their HR process.
Amazing commentary Korn, thanks for sharing! I saw a similar analysis at the ABA’s project to predict the “next big thing” but this article is very specific and interesting to see all these visualizations.
Totally agreed on your points mentioned about the article. Especially, I cannot agree more for the points about i) why don’t we apply the popularity of the songs as an outcome variable rather than the uniqueness of BTS (at the end of the day, other K-pop artists should be equally “unique” in some other variables), and ii) NLP analysis for the lyrics could be very insightful.
Also it is interesting that there are many available Korean-specific lexicon for sentiment / NLP analysis! Data science in Korea seems very advanced:) (for example: Korean-specific BERT model (KR-BERT) http://ling.snu.ac.kr/)
Amazing commentary to read, thanks for sharing these! Not only was it fascinating to see the e-sports leveraging people analytics both for the individual level and teams, but also the concerns about the article’s lack of focus in human beings is absolutely critical.
I thought that this IBM project is going to benefit the players to make a fair evaluation of their performance with transparency than counting on just the scores or people’s judgements. Completely agreed that the payment issue or ethics need to be prioritized much more than this scoring system. Really hope that more established employment structure is created to protect these players like in the other sports industries as soon as possible.