Are certain legislative bill topics more likely to pass?

Data science is able to detect anomalies, such as fraud and security anomalies. Can it also be used to find sneaky political practices? Quantifying and featuring-izing the “messy” world of politics can elucidate order and truth. Politics is one of the most important yet uninteresting place to the public, who have everything to gain and lose from every day decisions. Note: This post was written before Trump was elected to office…

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Clinton v. Trump: Candidate Sentiment from the 1st Presidential Debate

We all should be sitting at the edge of our seats in the next couple of months. Change is inevitable but the change agent may be questionable. To get psyched for this last stretch before the elections, I apply natural language processing (NLP) on this week’s first presidential debates with a focus on polarity in sentiment. Visualizations in this post include interactive candidate polarity graph and word clouds.  Continue reading “Clinton v. Trump: Candidate Sentiment from the 1st Presidential Debate”

Social Listening on Twitter v1.0: Sentiment by Word Approach

*Social media* has a many layers and, in the business and data senses, it is growing up nicely. Social sharing platforms provides developer access to their data, such as membership interactions and status updates, which can come as emotional outpourings, diatribes, celebrations, and affirmations. Armed with time (most difficult thing to come by on this list), stack overflow, reference materials, and an open source coding tool — anyone can quickly #oneup your *social media listening* skills. Not a bad skill to flaunt around, since positions managing and creating content on social media are increasing and relevant in every sector and job function. Now, adding the third word – listening – gives social media scouring, participating, and downloading another lift in professionalism.

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