Automated survey methods

Thank you for tuning in to this audio only podcast presentation. This is week 133 of The Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for The Lindahl Letter is, “Automated survey methods.”

I’m still spending some time digging into notebooks. This time around the topic as you might have guessed for that inquiry is figuring out how to automate a survey or more pointedly some sentiment analysis. People are building automated phone surveys with interactive voice response (IVR) systems. The next wave of this technology will be hard to tell if it is a person or a bot. Seriously, those systems are going to keep getting better at a rapid pace. The new wave of generative large language models are going to make outbound call surveys better and probably more plentiful. When the outbound call survey plugins roll in for ChatGPT, Bard, and anybody can build one for Llama 2 if they are willing to serve up a custom model. At the same time, I’m entirely sure (and hopeful) that people will be using more advanced technology to block those phone calls as well.  

All right, let’s shift away from considering phone calls and start to dig around into some of the automated sentiment analysis techniques that exist. We are starting to see frameworks where you can ask these new series of ChatGPT type services to act as an agent for you and complete some type of tasking. One of the things that would be interesting to ask that type of agent to complete would be to evaluate sentiment about something. I’m sure brands would like to have some automated brand evaluation methods. This will inevitably be used for politics as well. Right now we are not to the point where everybody has plugins that allow agency for ChatGPT or other toolings at the moment. That really is coming very soon as far as I can tell. Between lower energy costs and the solid platforms being built, those changes together may enable the compute for this type of interaction to happen in very conversational ways with a computer in the next 5 years.

Right now you could start by messing around with Google Colab and use the forms options they have [1]. Completing some really solid sentiment analysis may require more than just focusing on the  Google Colab environment. You may want to go out to somewhere like Hugging Face to get some information on how to do this with some python code [2]. A nice place to go along this journey to get some sentiment analysis done would be to venture out to the world of Kaggle and access one of their notebooks for sentiment analysis [3].Another notebook that I liked was from notebook dot community and it shares some of the natural language processing basic of sentiment analysis in really good chunks that make it easy to understand the mechanics of how things are happening [4]. At this point in the process you are probably ready to start to do some work on your own to complete some sentiment analysis and I found the right Google Colab notebook for you to start work on designing your own sentiment analysis tool [5].

I was talking to somebody recently about the future of AI. My explanation may have not been what they expected to hear. Within the next couple of years I expect to see a lot of companies spin up and a lot of different creativity happening in the space. All of that will end up settling out into a commodified built in series of advancements. A lot of new features for applications and tooling will spin out off the great wave of AI builds that are happening now, but it will end up feeling more commonplace and build into technologies that exist now. These technologies will mostly supplement or augment things as we move forward. You will have to know how to interact with and work with the generative models that exist, but they are going to be built into the platforms and systems that end up winning out within the business world in the next couple of years.







What’s next for The Lindahl Letter? 

  • Week 134: The chalk model for predicting elections
  • Week 135: Polling aggregation
  • Week 136: Econometric models
  • Week 137: Time-series analysis
  • Week 138: Prediction markets

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