Please clap on medium if you like this article, thank you! :)īio: Ezz El Din Abdullah ( ) is a Data Platform Engineer at Affectiva.Macros for scripting ActionScript. If you want to see more tutorials blog posts, check out: INFOGRAPHIC: Steps To Perform Text Data Cleaning in Python.10 command-line tools for data analysis in Linux.In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Fransisco, US, August 2016 "Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media”. Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly.We learned how to use tr to translate characters, used grep to filter out words starting from 3 letters, used sort and uniq to get a histogram of word occurrences, used awk to print fields in our desired positions, used sed to put the header to the file we’re processing. The clickbait paper suggests much deeper investigations than we did here, but we could get some valuable insights with just one line of code at the command line. We can see here non-possessives like australian, president, obama and some other words that can happen in both. For the clickbait data, we want the most common 20 words to be represented like this with their counts: Let's see what the final output first that we want to get. If you list what's inside the container you'll see two text files called clickbait_data and non_clickbait_data. Let's clean two text files containing clickbait and non clickbait headlines for 16,000 articles each. This data is used from a paper titled: Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media at 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Our goal here is to get the most common words used in both clickbait and non-clickbait headlines. If using docker is still unclear for you, you can see why we use docker tutorial
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