From Toronto to Tokyo, workplace politics transcends business, language, and geography. Behind each damaged workforce or excessive turnover fee typically lies a delicate battlefield of energy video games. However what if we may decode these dynamics not simply anecdotally, however at scale – utilizing the uncooked, emotional knowledge of actual individuals’s experiences? That is the promise of making use of machine studying and sentiment evaluation to giant swimming pools of office discourse. Reddit, with its semi-anonymous honesty, acts as a residing database of company confessionals. By coaching ML fashions on Reddit threads about office sabotage, alliance-building, and blame tradition, we see recurring themes: self-preservation disguised as management, loyalty undermined by quiet aggression, and the gradual suffocation of innovation beneath managerial insecurity.
What the Machines See
Utilizing state-of-the-art sentiment fashions like SiEBERT and matter modeling engines like BERTopic, we processed hundreds of Reddit entries. The algorithms sorted content material into emotionally loaded classes: betrayal, frustration, helplessness, ethical outrage. Threads with phrases like “she took credit score for my mission,” “they pushed me out,” or “I used to be scapegoated in entrance of the entire workforce” typically scored as extremely detrimental with over 85% confidence. Curiously, emotionally uncooked posts tended to obtain extra engagement, which means that workplace dysfunction isn’t simply widespread – it’s magnetic. The fashions additionally…
