Sidd Sachdeva

Sidd Sachdeva

AI for social good

Flipside Research

Published:

Code for my Flipside Research is in a Private Repo, but reach out if you’re interested, I can share it in some cases

Main Claim Detection Algorithm

I developed an algorithm that built on deep sentence representations, and combined extractive text summarization techniques with supervised subjectivity classification to identify the main opinions in a piece of persuasive writing. Traditional approaches that performed well in the literature of a Naive Bayes-Support Vector Machine only achieved 55% accuracy on this task, but my approach achieved 83% accuracy. In follow-up experiments examining the mistakes, I learned that this was because subjectivity in news and opinion requires more nuanced understanding than in product reviews where the literature evaluated on to perform well.

Automatic Live Clustering

Code

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Motivated by research on casual information visualization, we built an interactive visualization of the different sides of an issue, based on people’s votes on a particular claim. We combined a d3.js and vue.js front-end with regular HTTP requests to the backend to dynamically update this visualization as people voted. The methodology for the clustering went as follows:

  • Construct Binary Agree/Disagree Votes Matrix
  • Perform Dimension Reduction (PCA, metric and non-metric MDS)
  • Perform Clustering on new representations (KMeans, MeanShift)
  • Automatic tune Hyperparameters based on Sillhouette Score
  • Construct Clustering Rationale using Statistical Feature Selection

Devil’s Advocate Recommendation System

Motivated by political psychology research on the effects of language, I developed a system that used perspective topic models combined with a recommendation system to recommend people opposing points of view tailored to them. This system was able to generalize accurate predictions of recommendations to new articles based solely on text.