Sentiment Analysis of Turkish Twitter Data

What is it about?

In this paper, we present a mechanism to predict the sentiment on Turkish tweets by adopting two methods based on polarity lexicon (PL) and artificial intelligence (AI). The method of PL introduces a dictionary of words and matches the words to those in the harvested tweets. The tweets are then classified to be either positive, negative, or neutral based on the result found after matching them to the words in the dictionary. The method of AI uses support vector machine (SVM) and random forest (RF) classifiers to classify the tweets as either positive, negative or neutral. Experimental results show that SVM performs better on stemmed data by achieving an accuracy of 76%, whereas RF performs better on raw data with an accuracy of 88%. The performance of PL method increases continuously from 45% to 57% as data are being modified from a raw data to a stemmed data.

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Why is it important?

We proposed two methods based on polarity lexicon and machine learning classifiers (e.g., SVM and RF) to predict the sentiment on Turkish tweets. RF performed better in classifying positive data on all three occasions. SVMperformed better than the other algorithms in classifying negative and neutral data in most cases when the data are stemmed. While RF achieved its best accuracy of 79.9% on stopword and 88.5% on raw data of the first and second datasets, respectively. SVM achieved its best accuracy of 76.4% and 67.6% on stemmed data of the first and second datasets, respectively.

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