Unveiling Hidden Intents: Using the Python Library "emotionics" as a Negotiation Radar Introduction Negotiations are often clouded by bluffs, excuses, and hidden agendas. What if you could objectively peek behind the curtain? Today, I will introduce how to leverage the Python library "emotionics" not just as an analytical model, but as a powerful, objective radar for negotiations. By using the gyo (estimation) module, we can strip away superficial emotions and uncover the true physical state of the negotiation table. https://pypi.org/project/emotionics/ Code Here is a practical example. Imagine a scenario where a business partner uses a macro-environmental excuse to cut down your orders. ‘’’Python import os import json import emotionics from dotenv import load_dotenv # Load environment variables from .env file load_dotenv() # 1. Activation # * Example using Gemini emotionics.activate(     llm="gemini",     api_key=os.getenv("MY_API_KEY"),     mo...

Emotionics Library Emotionics is a lightweight experimental library for exploring how emotional signals can be abstracted from text using LLMs and a structured emotional framework. pip install emotionics Emotionics does NOT: - diagnose mental states - judge individuals - predict behavior - manipulate emotions It estimates emotional signals in text, nothing more. Reasons I built emotionics because purely statistical or purely linguistic approaches felt insufficient to describe how humans actually react, decide, and communicate. LLM × Emotionics = emotionics library Emotionics does not replace LLMs. It works with LLMs by providing a structured lens to interpret emotional patterns already present in text. What the emotionics library can do • extract emotion-related signals • map them to a structured schema • experiment with temporal or contextual tendencies Expected Users This library is intended for: • researchers • developers interested in human-centered AI • pe...

 I wrote Python code which shows Correlation between NASDAQ price, Corn and Gold(Corr=0.945). Corn and Gold are best mix I have found. https://gist.github.com/Kouhei-Takagi/a1a9b073f6cd6dacb7eb1ea871d8fd3c I changed some important parts. https://gist.github.com/Kouhei-Takagi/5becaf7c3a24d63ac9c56856e81297f2

  2024/4/8.  Secret Code These days, I wrote some Python codes about searching correlation between Nasdaq and Commodities. The latest score is 0.95189 now(2024/4/8). I don’t open this code because I will use it for my trading assistant program. I hope I can be rich with the program… (Additional things...) I changed my mind, but it is only small... This is the result picture between Predicted model vs Actual Nikkei 225 Close price. 2024/4/17.  Additional result I lost $200(¥30000)... My bad point is too much believing my thought...

 Nasdaq Correlation with Cacao, Copper and Sugar Nasdaq = (Cacao * 0.586) * (Copper ^ 2.528) - (Sugar ^ 3.965) Correlation score is 0.64978. <script src="https://gist.github.com/Kouhei-Takagi/6eb89fb0dc310df733ddab2491a55e72.js"></script>

  (cacao_close ** 0.31) + (copper_close ** 0.46) - (suger_close ** 0.37) This shows highest correlation with Nikkei 225. The value is 0.631. I did great work! I uploaded whole code in below github. https://github.com/Kouhei-Takagi/PythonAlmostEveryday/tree/main/VSNikkei

 2022/11/15.  NIKKEI225&DJIA&NASDAQ Prediction Code(Python) I wrote some Python Codes. https://github.com/Kouhei-Takagi/NIKKEI-Price NIKKEI225 prediction Code   DJIA prediction Code NASDAQ Prediction Code

  2022/1/16.  Nikkei index price analysis with Python I wrote some Python codes for analysis about Nikkei index price. The time period is 2021/10 - 2021/12, and I uses Nikkei index price, Volume and Selling Volume as parameters. The codes are on GitHub. https://github.com/Kouhei-Takagi/NIKKEI-Price PPDAC is important for this task, PPDAC is consisted from Problem, Plan, Data, Analysis and Conclusion. {Ploblem} I want to know what happened about movement of Nikkei index price. {Plan} Can I know it from some parameters? {Data} I collected data with some Python codes. {Analysis} I visualized the data and thought about it. 2021/10 2021/11 2021/12 These figures show that (A) Volume and Selling Volume had strong relationships, and (B) perhaps Nikkei index price downed after 5 -10 day later when Selling Volume increased.   {Conclusion} Not secret. From thinking about above (A) and (B), Nikkei index price may be difficult to fall down or raise u...

 I did Python coding and wrote one article on Qiita. https://qiita.com/K-TKG/items/cb198a0f63f98003d961 This is written in Japanese, so that perhaps you are not able to read it... Parts of my code are below... https://github.com/Kouhei-Takagi/NIKKEI-Price