Lexicon-Based Sentiment Analysis at GHC 2014
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2014 Lexicon-Based Sentiment Analysis Using the Most- Mentioned Word Tree Bo-Hyun Kim, Sr. Software Engineer HP Big Data Business Unit Oct 10 th , 2014 #GHC14 2014
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Attended Grace Hopper Celebration to present the work in Data Science Track. The presentation is on using HP Vertica Pulse and enhancing the accuracy using the right pre-processing methods and training for accuracy using the naive bayes theorem.
Transcript of Lexicon-Based Sentiment Analysis at GHC 2014
2014
Lexicon-Based Sentiment Analysis
Using the Most-Mentioned
Word TreeBo-Hyun Kim, Sr. Software Engineer
HP Big Data Business Unit
Oct 10th, 2014
#GHC14
2014
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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!
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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]
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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.”
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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
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Product Review
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Data… Data… Data…
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HP Vertica Pulse
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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
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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
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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
− 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
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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
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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”
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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
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Tree View
healthcare
obamacare !(Obamacare)
obama !(Obama) !(health)health
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Thank you
Many thanks to*:Tim Donar, Solution Engineer
Beth Favini, Tech Pubs Sr. Manager
Judith Plummer, Tech Pubs Editor in Chief
* In alphabetical order
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Got Feedback?
Rate and Review the session using the GHC Mobile App
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