Sentiment analysis using a random forest classifier on turkish web comments

Abstract

Sentiment analysis is an active research area since early 2000s as a field of text classification. Most of the studies in this field focus on the analysis using the text in English language, where the Turkish and the other languages have fallen behind. The purpose of this research is to contribute to the text analysis in Turkish language using the contents that we access through web sites. In particular, we deduce the sentiment behind noisy product reviews and comments in a highly popular commercial web page. In this context, we generate a unique dataset that includes 9100 product review samples for training our classification model. There are different word representation methods that are utilized in sentiment analysis, such as bag-of-words and n-gram models. In this work, we generated our word models using the word2vec algorithm. In this model, each word in the vocabulary is represented as a vector of 300 dimensions. We utilize 70% of our dataset in the training of a Random Forest Model and make binary classification of sentiments as being positive or negative, utilizing the ratings of the user for the product as classification labels. In the highly noisy and unfiltered comments, we achieve an accuracy of 84.23%.