This document discusses sentiment analysis and how HP Vertica Pulse performs lexicon-based sentiment analysis on tweets to determine sentiment polarity. It explains that HP Vertica Pulse first prunes tweets to remove irrelevant data, then uses a Naive Bayes classifier trained on labeled tweets to assign sentiment scores. Tuning parameters like positive/negative word lists can further improve accuracy. Analysis of the most frequent words using a generated word tree can provide more targeted and accurate sentiment scores by topic.

1. 2014
Lexicon-Based
Sentiment Analysis
Using the Most-Mentioned
Word Tree
Bo-Hyun Kim, Sr. Software Engineer
HP Big Data Business Unit
Oct 10th, 2014
#GHC14
2014

2. 2014
What to Expect
 Sentiment Analysis
− What is it?
− Why is it interesting?
− How HP Vertica Pulse works
− Achieving greater accuracy
− Different point of view using the most-
mentioned word tree

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What I Expect
 A 5-star rating on GHC app
 I just expect you to enjoy and learn!

4. 2014
Sentiment Analysis
 In plain English
− the process of automatically detecting if a text
segment contains emotional or opinionated
content and determining its polarity (e.g., “thumbs
up” or “thumbs down”), is a field of research that has
received significant attention in recent years, both in
academia and in industry. [Wright, 2009]

5. 2014
Gimme Examples!
 Also known as:
− Opinion Mining
− Text Mining
 Determine people’s general opinion
− “I just got a new car, and I’m loving it ”
− “My new car isn’t as fast as I thought.”

6. 2014
Why are we interested?
 Increasing(every minute!) web usage
− Articles
− Blogs
− Comments
 Power of Social Media
− Online Shopping
− Customer Reviews
− Recommended products on Amazon
− How other people feel about the product

7. 2014
Product Review

8. 2014
Data… Data… Data…

9. 2014
HP Vertica Pulse

10. 2014
How to Analyze?
 Lexicon-based approach – HP Labs [Zhang et. al. 2011]
 Choose a product, person, event, organization, or topic
[Hu and Liu, 2004] to analyze the opinion
 Determine the Semantic Orientation score of opinion
lexicons
Word Semantic Orientation Value
Fabulous +3
Good +1
Bad -1
Nasty -3

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Sentiment Scoring
 Input: text or sentence
 Output: For each attribute or entity, generates a sentiment score
ranging from -1 to 1
− -1: Negative sentiment
− 0: Neutral sentiment
− 1: Positive sentiment
 Entity-level lexicon-based sentiment scoring

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Limitation
 Semantic Orientation value(‘missed’) = -1
 Gives more weight to the closely located
word
 Accuracy can suffer

13. 2014
Improve accuracy
 Accuracy is what we strive for!
 More robust pre-processing
− Prune data to fit for different types of user
opinion (e.g. Twitter vs. YouTube comments)
 Naïve Bayes Classifier Training
 Tune accordingly

14. 2014
Data Set
 Test dataset
− Stanford students collected
− In 2009
− Over 3 million tweets with tested score
− Analyzed 3500 tweets
 Collected dataset
− HP Vertica Pulse Twitter Connector
− In 2014
− Total of 1.2 million tweets over 30 days

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Data Pruning
 Remove
− Job postings
• #job, #jobs, #tweetmyjob
− Links
• http://this.is/nogood
− Duplicates
− Twitter specific characters
• RT, @, #
− Emoticons
• I hate my life :-), sarcasm is wide-spread disease
 After pruning
− ~287000 tweets, 24% of the 1.2 million tweets

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Naïve Bayes Classifier
 Supervised learning
− Probabilistic classifier based on Bayes’ theorem
− Requires a small amount of data
− Assumes the presence/absence of a particular
feature of a class is unrelated to the
presence/absence of any other feature
− Classifying the object based on its included features
𝑃(𝐶𝑗|𝐷) =
𝑃 𝐷 𝐶𝑗 𝑃(𝐶𝑗)
𝑃(𝐷)
− Open source found at [nltk.org]

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Naïve Bayes Classifier
 Results:
− Final accuracy : 0.788

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Tuning Pulse
 Positive words
 Negative words
 Neutral words
 White lists
 Stop words
 Synonym mappings

19. 2014
Accuracy Comparison
 Sentiment scores generated for each
phase
Keyword Ideal Original Pruning Training Tuning
Healthcare -0.1515 -0.0333 -0.0833 -0.1 -0.125
Obama 0.308 0.0944 0.1535 0.1535 0.1842

20. 2014
Trend/Targeted Analysis
 Targeted dataset analysis can help improve accuracy
 Identify the most-mentioned words
− Use the most-recurrent words to narrow the scope of analysis
 Find new trends
− Government healthcare (2009) vs. Obamacare (2014)
 Are we looking at the targeted data?
− “Solve healthcare challenges with technology!”
− “Healthcare After ObamaCare”
− “Get affordable healthcare at HealthCare.gov”

21. 2014
Generating Tree
 Increase the relevancy of sentiment score by
running the sentiment analysis on the entity, as
well as on the most-recurrent words to identify:
− Homonyms that machines do not understand
− More accurate scores based on user interest
 Generate tree using Text Search
− Merge stemmer words
e.g. query, queries, querying…
− Lucene - apache open source

22. 2014
Tree View
healthcare
obamacare !(Obamacare)
obama !(Obama) !(health)health

23. 2014
Thank you 
Questions?
bohyun@hp.com
bohyun.j.kim@gmail.com
Many thanks to*:
Tim Donar, Solution Engineer
Beth Favini, Tech Pubs Sr. Manager
Judith Plummer, Tech Pubs Editor in Chief
* In alphabetical order

24. 2014
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