The value for automated sentiment analysis is when there's enough data to get interesting breakdowns instead of just a big average. Like, the average sentiment for tweets mentioning your service doesn't give you anything you don't know; however, if you: see in real-time monitoring that suddenly as of 13 minutes ago the metric drops to the floor; see that out of the 123 markets you operate in, a couple are much better/much worse than the average - something you wouldn't notice from outside of those markets; see that there's a big difference in the sentiment coming from male or female users; can track sentiment separately for specific attributes of your products and see that for product A everyone hates an aspect that's fine for other products those things can become actionable info. More on reddit.com

annotate your dictionary - when I did this I added words like 'fud' 'centralized' 'pump' 'dump' 'scam' etc to negative list and 'moon' 'lambo' etc to the positive list scale each tweets sentiment by retweets or views. Joe schmo's tweet shouldn't carry the same weight as Big Influence Bob. the bitcoin market was/is heavily manipulated . doesn't matter what average investor thinks when the price movement comes from big money. You need much more data on price/technicals, especially regarding dates that futures contracts expire More on reddit.com

Not sure how I feel about this More on reddit.com

Are you using ChatGPT o3? If not, I would try that. If that doesn't get you where you need to be, if you haven't done this already, I would describe in detail to ChatGPT what I'm trying to accomplish, the results I'm getting, include some specific examples of problems, and end with, "I want your help drafting a prompt that will alleviate these issues. Before we do that, what clarifying questions do you have?" Then after you answer the questions, have it write the prompt you need. Tweak if necessary and then run your prompt. If the end results are what you need, great. Remember that models matter, you can go meta and ask AI for help writing AI prompts, and telling an LLM to ask clarifying questions before doing anything complicated usually improves results. And if the end results are still sub-per, which is possible because LLMs overall are only about as competent as college students, then I think I'd probably just submit whatever it provides, along with a disclaimer that you don't consider the results to be equal in quality to what you can achieve on your own. I'd also include a CYA in an email briefly outlining your efforts to maximize quality, so they understand that the problem is the LLM, not you. More on reddit.com