Updated Sentiment Analysis and a Word Cloud for Netflix - The R Way!

The Netflix investors must be happy and cheerful as the stock is up more than 78% since the beginning of the year (YES, 78%, Source: Yahoo Finance!).  I am not going to talk about what turned the stock around after a much talked/hyped about Netflix debacle of the late 2011 that earned Reed Hastings quite a few UNWANTED title and every one demanded his resignation from the top post.  Not so fast, Mr. Bear!  Reed Hastings must be smiling!  After a stellar performance this year including carefully released stats on viewership, streaming hours as well as a solid Q4'11 earnings, Netflix is back and most importantly viewers are back!

Well, is is not coincidental that the sentiment for Netflix is also improving, 68% of the tweets now have positive sentiment.  See the table below:

Total	Positive	Negative	Average	Total	Sentiment
Tweets  Fetched	Tweets	Tweets	Score	Tweets
499	171	80	0.281	251	68%

*Make sure you understand and interpret this analysis correctly. This analysis is not based on NLP. 

I updated the sentiment analysis that I did last year, http://goo.gl/fkfPy ,  (I was then just beginning to play with Twitter and Text Mining packages in R) and used advanced packages like "TM" and  "WordCloud".  The new analysis is based on more than 6,800 words which are most commonly prescribed in various sentiment analysis blogs/books. (Check out Hu and Liu http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html)

I came across this excellent blog by Jeffrey Bean, @JeffreyBean, (http://goo.gl/RPkFX) and his tutorial. Thank you Mr. Bean!  Please follow the instructions from Bean's slides and the R code listed there as well as the R code here:

Here is the updated R code snippets -
#Populate the list of sentiment words from Hu and Liu (http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html)

huliu.pwords <- scan('opinion-lexicon/positive-words.txt', what='character', comment.char=';')

huliu.nwords <- scan('opinion-lexicon/negative-words.txt', what='character', comment.char=';')

# Add some words

huliu.nwords <- c(huliu.nwords,'wtf','wait','waiting','epicfail', 'crash', 'bug', 'bugy', 'bugs', 'slow', 'lie')

#Remove some words

huliu.nwords <- huliu.nwords[!huliu.nwords=='sap']

huliu.nwords <- huliu.nwords[!huliu.nwords=='cloud']

#which('sap' %in% huliu.nwords)

twitterTag <- "@Netflix"

# Get 1500 tweets - an individual is only allowed to get 1500 tweets

 tweets <- searchTwitter(tag, n=1500)

  tweets.text <- laply(tweets,function(t)t$getText())

  sentimentScoreDF <- getSentimentScore(tweets.text)

  sentimentScoreDF$TwitterTag <- twitterTag

# Get rid of tweets that have zero score and seperate +ve from -ve tweets

sentimentScoreDF$posTweets <- as.numeric(sentimentScoreDF$SentimentScore >=1)

sentimentScoreDF$negTweets <- as.numeric(sentimentScoreDF$SentimentScore <=-1)

#Summarize finidings

summaryDF <- ddply(sentimentScoreDF,"TwitterTag", summarise, 

                 TotalTweetsFetched=length(SentimentScore),

                 PositiveTweets=sum(posTweets), NegativeTweets=sum(negTweets), 

                 AverageScore=round(mean(SentimentScore),3))

summaryDF$TotalTweets <- summaryDF$PositiveTweets + summaryDF$NegativeTweets

#Get Sentiment Score

summaryDF$Sentiment  <- round(summaryDF$PositiveTweets/summaryDF$TotalTweets, 2)

Saving the best for the last, here is a word cloud (also called tag cloud) for Netflix built in R-

I will be putting the R code up here for building a word cloud after scrubbing it.

Happy Analyzing!

Eight Big Data, BI, Cloud Related Trends from Accenture 2011 Technology Vision

• Data takes its rightful place as a platform.
• Analytics is driving a discontinuous evolution from business intelligence. (This is most controversial...)
• Cloud computing will create more value higher up the stack.
• Architecture will shift from server-centric to service-centric.
• IT security will respond rapidly, progressively—and in proportion.
• Data privacy will adopt a risk-based approach.
• Social platforms will emerge as a new source of business intelligence.
• User experience is what matters.

Source: http://www.accenture.com/us-en/technology/technology-labs/Pages/insight-accenture-technology-vision-2011.aspx

Fast Emerging, Developing and Developed Trends in Business Intelligence (BI)

1. Emerging - Social and Collaborative
2. Developing - Location Analytics (Augmented Reality for Mobile Devices)
3. Developed - Mobile

Facilitators:
- Huge amount of investment in collaborative software to optimize performance
- One obvious one - explosion in Mobile device and heterogeneous devices are accepted in the organization
- Strong desire to tie the analytics to its source, where did the event happen - zero in on the location. 

I get to live, rationalize, strategize, visualize, evangelize and everything else to influence build real BI solutions.
More on this in my follow up blogs, for now, enjoy following excerpts from a Gartner report -
....
Within two years, 15% of BI deployments will combine collaborative and social decision making-environments.
....
With the rapid development of handheld functionality and mass adoption rates across enterprises, by 2014 33% of business intelligence will be through mobile devices. 
....
Source: Gartner Predicts Business Intelligence Will Go Mobile and Social