1. A light based visualization of sentiment in Tweets
BiasBeacon
A light based visualization of sentiment in Tweets
By: Stephen Bunn, Preston Wilson, Ian Watts, Bryce Cloniger
Stephen

2. Table of Contents Our Goal Inspiration Color Model Equipment
Limitations
Application
Demonstration
Gained Experience
Citations
Table of Contents
Stephen

3. Our Goal Wanted to visualize the mood of Twitter tweets as color
Gain experience working with…
Python
Social media APIs
Building HTTP requests
Basic sentiment classification
Version control
Preston

4. Inspiration
North Carolina State University research project “Sentiment Viz” by Healey and Ramaswamy.
Grabs the most recent tweets given a term
Visualizes those tweets on an emotional scale
Allows the user to interact with the results
We stole Michael Yang’s idea...
Ian

5. Sentiment visualization with Tweets using the term “grand canyon”
Ian
Sentiment visualization with Tweets using the term “grand canyon”

6. Color Model Russell's Model of Emotional Affect
Maps terms on a two-dimensional scale between arousal and pleasantness
The ANEW (Affective Norms of English Words) dataset maps english words to this model
Utilized HSL (Hue, Saturation, Lightness) to map this model to color
Utilized a Python module named HUSL, to convert HSL to RGB
Bryce

7. Russel’s Model of Emotional Affect applied to the HSL color format
Bryce
Russel’s Model of Emotional Affect applied to the HSL color format

8. Equipment Python was used to develop our project
Used the Philips Hue Go as our beacon
Used the Tweepy module to collect tweets
A user built “ANEW” dataset by Tom Lee
Ian

9. Limitations We could not get a copy of the real ANEW dataset
Used Tom Lee’s derived ANEW dataset
We also could not set up our Philips Hue bridge on ASU’s layered network
Setup our own network using a consumer router
Stephen

10. Our Application

11. Getting the tweets
Used the Tweepy Python module and Twitter’s search API
Retrieved a defined amount of Tweets given a passed search term
import tweepy oauth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_TOKEN) oauth.set_access_token(ACCESS_KEY, ACCESS_TOKEN) api = tweepy.API(oauth) tweets = tweepy.Cursor(api.search, q='#tweepy').items(list)
Bryce

12. Classifying the tweets
Basic sentiment classification using the ANEW dataset entries
health, v = (mu: 6.81, sigma: 1.88), a = (mu: 5.13, sigma: 2.34), freq = 105 win, v = (mu: 8.38, sigma: 0.92), a = (mu: 7.72, sigma: 2.16), freq = 55
We use the probability density function of a normal distribution to estimate the probability density of the word’s rating falling exactly at the mean.
Then using a weighted average we can find the mean valence and arousal.
Mv = 7.76, Ma = 6.48
Stephen

13. Displaying the mood
Wrote a basic client to handle updating the state of our beacon
Also handles discovery and management of the Philips Hue Bridge
import bias_beacon beacon_client = bias_beacon.BeaconClient( bridge_ip=bias_beacon.utils.mac2ip(BEACON_BRIDGE_MAC), username=bias_beacon.glbl.MODULE_NAME, beacon_uid=BEACON_UID ) beacon_client.update_on(True) beacon_client.update_color(255, 0, 0)
Preston

14. A plotting of 1000 tweets using the term “hate” or the hashtag #hate
Preston
A plotting of 1000 tweets using the term “hate” or the hashtag #hate

15. Demo

16. Russel’s Model of Emotional Affect applied to the HSL color format

17. Gained Experience Learned about basic sentiment classification
Used Python to implement communication between Twitter and the Hue
Learned how to use version control systems such as GitHub
Gained experience working as a team
Ian

18. Questions?

19. Citations Twitter Visualization
Russel’s model of emotional affect
Tom Lee, ANEW dataset
Tweepy
Philips Hue API