Sentiment analysis of sentences in Serbian language

Nikola Milošević

Why to analyze sentiment in Serbian?

● Great industrial need– Ads websites– Automated market research– Customer satisfaction

● NLP tools for Serbian are not developed– Need for tools and resources– Almost no accessible tools through API

Serbian language

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

writing system

Sentiment analysis work-flow

Tokenization and preprocessing

● Process of breaking a stream of text up into words

● Stop-word filtering● Negation handling

– Adding NE_ prefix after negation– All words before punctuation

● Irregular verbs

Stemming

● Process for reducing inflected words to their stem, base or root form

● Kešelj and Šipka (2008)● Hand crafted rule based stemmer● ~300 rules

Sentiment analysis

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

Naive Bayes

● Algorithm that learns fast● Bag of words approach● Assumption of conditional independence● Laplace smoothing

Implementation

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

Results

● Stemmer– Smallest and most precise stemmer– 90% correct on news articles– Problems: small words, irregular inflections,

voice changes

● Sentiment analyzer– 80% correct– Problems: Irony, ambiguity, small training

data

Future work

● Stemmer– Use snowball framework

– Build multi-step stemmer

● Sentiment analyzer– POS tagging

– Complex negation handling

– SVM algorithm

Thank you

● Available from http://inspiratron.org

● Contact: nikola.milosevic@postgrad.manchester.ac.uk