This week we focused on two major items: finishing our survey IRB, and continu make progress on sentiment analysis. We’ve gotten a lot done, and are happy to have submitted our IRB, along with our survey! After long discussions, we have come to the conclusion that we will most likely have to proceed with either the nudge experiment– aimed at detecting bias against electric vehicle owners– or with the topic classification. This will primarily depend on how well the topic classification can be done, which we are still investigating. At this point we are waiting to hear back from IRB, and also from a large panel of EV drivers, to see if we will be able to use them as a data source.
We’ve made a lot of progress with sentiment analysis this week. We’ve spent a lot of time learning about different kinds of neural nets, and have done a lot of literature review to determine which models are likely to work best for our particular problem. Currently, we have trained a recurrent neural net using the Keras API with TensorFlow. It seems to be out-performing our past bag-of-words approach, and we are currently investigating it further to determine just how well it works, and how we can improve upon it. One of the methods we are planning to use to improve this performance is to use Bayesian optimization for hyper-parameter tuning of our model. We will also be constructing a convolutional neural network and seeing which one is better fit to accomplish our sentiment task. While we’ve been improving our sentiment classification, we’ve also started doing analyses of sentiment based on our SVM’s predictions. The idea here is that we can work on both aspects of the problem at the same time, and once our sentiment predictions are improved, we’ll update the inputs to our analyses and see a more accurate analysis of the EV infrastructure.
We’re very excited to get the survey kicked off, and to see some results from our sentiment analysis!