AAAI Publications, Tenth International AAAI Conference on Web and Social Media

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Analyzing the Political Sentiment of Tweets in Farsi
Elham Vaziripour, Christophe Giraud-Carrier, Daniel Zappala

Last modified: 2016-03-31

Abstract


We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.

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