While week three was filled with quick wins, week four has been a slow trawl to make progress on critical objectives. A lot of time this week was focused on realignment with our faculty mentor, Professor Asensio, on what was necessary for our review categorization ML training set. After long discussions, we’ve decided to pivot away from using MTurk to utilizing two different tools: Qualtrics and PlugInsights. Qualtrics offers a crowdsourcing platform that higher participant demographic fidelity in comparison to MTurk. PlugInsights is a spin-off crowdsourcing platform from PlugShare, the company that provided us with the original dataset. The bonus of PlugInsights is all participants from the platform are EV drivers, immediately lending them credibility in understanding the nuance in the reviews we would ask them to classify.
In terms of sentiment analysis, after additional feature augmentation and hyper-parameter tuning, we’ve reached the peak of feasible performance with SVMs. Time was spent this week exploring neural network based learning algorithms and understanding how one could be properly implemented for our domain specific problem.
We also tried utilizing an SVM for the review classification problem on a small training set of 1,300 reviews. We reached around ~50% accuracy, which is reasonable considering the difficulties of multi-label data and the size of our training set, but here again, we’ve decided to look towards other methods. We’re excited to see where our new plans will lead us!