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TweeVee

TweeVee displays the trends of sentiments of TV shows' audiences based on their tweets on Twitter. It allows comparison of sentiments among the shows over a period of time.

We wanted to include multiple years of data. Due to lack of knowledge and resources, we worked on only 7 days of data. However, this data can be used to see differences in sentiments of audiences before/during/after the telecast of weekly shows.

The visualization shows total number of tweets and average sentiment value of those tweets per day per TV show for past 7 days. The sentiment values are plotted on y axis on a rank of 1 to 10. The more positive the tweets, the higher they rank on the y-axis.

We follow a workflow to collect the data, process it and plot it in data visualization for each of the TV shows. The steps we follow are:

  • Collect 7 days' worth of tweets for the respective hashtag of the TV show using Twitter API.
  • Extract and organize the relevant information from tweets in an array.
  • Assign sentiment to each tweet using Sentiment140 API.
  • Store the organized data to .txt files (one file is allocated to one TV show).
  • Retrieve data from the files and remove duplicate entires.
  • Convert the data to JSON objects and store them in file.
  • Retrieve JSON objects.
  • Plot the data using D3.

We used a fixed number of TV shows for demonstration purposes. We tried to cover multiple genres in our selection. The TV shows we included, with their respective hash tags, are:

Animations

  • Family Guy - #familyguy
  • South Park - #SouthPark

Comedy

  • 30 Rock - #30Rock
  • How I Met Your Mother - #HIMYM
  • Modern Family - #ModernFamily
  • New Girl - #NewGirl

Drama

  • Bones - #bones
  • Criminal Minds - #CriminalMinds
  • The Walking Dead - #TheWalkingDead

Reality Shows

  • Dancing With The Stars - #DWTS
  • Mythbusters - #MythBusters
  • The Voice - #TheVoice

Team Members and Roles

  • Derek Kan - sentiment analysis, main back-end functionalities
  • Suhani N Mehta - data collection from Twitter, additional back-end functionalities
  • Raymon Sutedjo-The -- interaction & interface design, data visualization

Technologies Used

  • Code - HTML, CSS, Javascript/jQuery, JSON, PHP
  • APIs - Twitter, Sentiment140

Demo Version

http://ray-mon.com/tweevee/

Known Bugs

  • The removal of duplicate entries has not been tested. We are unsure if it functions properly.
  • Most of the tweets are either positive or neutral (no negatives) based on Sentiment140.
  • The Sentiment Analysis is not 100% accurate. For example, if a person tweets a dialogue from the show, the analysis is done of the dialogue instead of the intention of the user who tweeted it.
  • We are filtering tweets based on hash tag values only. They may not necessarily be of the TV show. For example, "#bones" may have been mentioned without allusion to the TV show.

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