This document describes an interactive Twitter analysis application that analyzes tweets related to an organization. The application displays tweets in graphical panels that show word clouds, top words, topics, sentiment, and interactions. The goal is to monitor Twitter activity, understand trends, and help plan an effective communication strategy. The analysis can be refined by adjusting the number of days and tweets analyzed. Potential improvements include comparing campaigns, filtering time periods, and expanding to other social networks.

1. Twitter analysis
Data as factor for designing the right
communication strategy.
http://es.linkedin.com/in/pcllm

2. Index
• Introduction
• Wordcloud
• Top 10
• Topics
• Sentiment
• Interactions
• Tweets and Cleaned Data
• Final thoughts
• Acknowledgements
• Demo

3. Introduction
Twitter analysis interactive scorecard:
• The application downloads and analyses tweets related to an Organization, being
published during the last few days. Once the data has been downloaded it is
displayed in a set of clickable tabbed panels showing graphical representations of
information from different perspectives.
• The idea behind this real-time analysis application is to monitor Twitter activity,
track trends, engagement and user interactions involved with the Organization, in
order to provide evidences of what’s happening or happened some days before,
and help understanding, diagnosing and planning a suitable Twitter
communication strategy that maximizes impact and increases funds.
• The amount of days to look back in time and the maximum number of tweets to
be downloaded, can be changed to refine the analysis on the sliders of the control
panel (in the left side). Hit the Go! button to launch the analysis according to the
selected values.

4. Word cloud
Wordcloud displaying the most used words in
different sizes and colors by frequency.
• Discover and classify the most used or important words that
verbalize your communication activities in Twitter social
network.

5. Top ten
Displays Top 10 used words, Top 10 most active
users, Top 10 users with highest tweet to retweet
ratio, and top 10 links referred:
• Find the most important or striking words being used.
• Target the most active users (influencers), to deal with.
• Discover useful, popular or significant information
sources (links appeared on tweets).

6. Topics
Displays topic evolution and related words:
• Track the evolution of the most used combinations of words
across time to monitor Topic trends on the last days.
• Discover word interrelation looking at a tree type word cluster
diagram (Dendogram).

7. Sentiment
Emotion and polarity of words:
• Determine the attitude of tweets according to an emotional
classification (joy, sadness, surprise, disgust and anger). Cloud
of words sized, colored and grouped according to its
emotional classification and frequency.
• Realize the amount of tweets distributed by emotion and
polarity (negative, neutral, and positive).

8. Interactions
Reply to a user and retweet a user graphs:
• Realise which users reply to a tweet of another user to
pinpoint who are the conversation makers.
• Watch the interaction between users retweeting other users
to discover the most active or referenced users (content
creators or promoters).

9. Tweets & Cleaned Data
Tweets:
• Watch the text from all downloaded tweets used for the
analyses in a table.
Cleaned Data:
• See the remaining tweet text after removing undesired
characters, hashtags, retweets, links and undesired words.
Once cleaned, this data is prepared for analysis.

10. Final thoughts
• This application performs an on-line interactive twitter
analysis that provides valuable information to monitor and
plan the communication strategy of an Organization.
• Further improvements to be done, in order to perform a
deeper and more flexible analysis of data:
– Segmented and comparative campaign analysis (key words or
hashtags).
– Constrain the period of time being analysed (set the beginning
and ending dates).
– Fine tune engagement and scope of tweets by measuring the
total number of followers for each user involved in retweets or
replies.
– Expand using data coming from other popular social networks.

11. Acknowledgments
Many thanks to:
• George Ross Ihaka, Robert Gentleman, et al. (R)
• Rstudio creators and mantainers (Rstudio)
• Winston Chang, Joe Cheng, et al. (Shiny)
• Jeff Gentry (twitteR)
• Ingo Feinerer, Kurt Hornik, Artifex Software, Inc. (tm)
• Timothy Jurka (Sentiment)
• Bettina Grün, Kurt Hornik (topicmodels)
• Hadley Wickham, Winston Chang (ggplot2)
• All other contributors to the amazing R comunity (stringr,
data.table, plyr, Rstem, gridBase, shinyBS, wordcloud, gridExtra,
igraph, ...)
http://es.linkedin.com/in/pcllm

12. Demo
Website: perell.shinyapps.io/twOrg
Remeber to Login using:
- Username: demo
- Password: org2015
http://es.linkedin.com/in/pcllm