Sentiment analisis of Serbian languge with stemmer for Serbian.

1. Sentiment analysis of sentences in
Serbian language
Nikola Milošević

2. Why to analyze sentiment in Serbian?
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Great industrial need
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Automated market research
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Ads websites
Customer satisfaction
NLP tools for Serbian are not developed
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Need for tools and resources
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Almost no accessible tools through API

3. Serbian language
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Belongs to Indo-Europian language group
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Slavic language
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Highly inflectional
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3 pronunciation types
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3 dialect groups
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Write as you speak
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Latin and Cyrillic
writing system

4. Sentiment analysis work-flow

5. Tokenization and preprocessing
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Process of breaking a stream of text up into
words
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Stop-word filtering
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Negation handling
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Adding NE_ prefix after negation
All words before punctuation
Irregular verbs

6. Stemming
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Process for reducing inflected words to their
stem, base or root form
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Kešelj and Šipka (2008)
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Hand crafted rule based stemmer
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~300 rules

7. Sentiment analysis
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Aim to build binary sentiment analysis
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General Serbian language
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No annotated corpus for Serbian
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Annotation work (~1000 small texts)
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Supervised machine learning

8. Naive Bayes
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Algorithm that learns fast
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Bag of words approach
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Assumption of conditional independence
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Laplace smoothing

9. Implementation
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Web API with presentation layer
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JSON communication
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Secured page for annotating
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Build using PHP and MySQL
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Web & Android

10. Results
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Stemmer
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90% correct on news articles
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Smallest and most precise stemmer
Problems: small words, irregular inflections,
voice changes
Sentiment analyzer
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80% correct
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Problems: Irony, ambiguity, small training
data

11. Future work
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Stemmer
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Use snowball framework
Build multi-step stemmer
Sentiment analyzer
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POS tagging
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Complex negation handling
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SVM algorithm

12. Thank you
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Available from http://inspiratron.org
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Contact: nikola.milosevic@postgrad.manchester.ac.uk