A Scalable Approach for Sentiment Analysis of Turkish Tweets and Linking Tweets to News
Published in IEEE, 2016
We present a framework for sentiment analysis on tweets related to news items. Given a set of tweets and news items, our framework classifies tweets as positive or negative and links them to the related news items. For the classification of tweets we use three of the most used machine learning methods, namely Naive Bayes, Complementary Naive Bayes, and Logistic Regression, and for linking tweets to news items, Natural Language Processing (NLP) techniques are used, including Zemberek NLP library for stemming and morphological analysis and then bag-of-words method for mapping.
Recommended citation: Kulcu, S., & Dogdu, E. (2016, February). A scalable approach for sentiment analysis of Turkish tweets and linking tweets to news. In 2016 IEEE Tenth International Conference on Semantic Computing (ICSC) (pp. 471-476). IEEE.