Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets

TL;DR

This study compares Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for emotion detection in Turkish tweets, finding NMF most effective for topic modeling and Random Forest best for classification.

Contribution

It introduces an n-level LDA method for emotion analysis in Turkish tweets and evaluates its performance against traditional models.

Findings

NMF outperformed other topic modeling methods.

n-stage LDA achieved the highest F1-measure among LDA variants.

Random Forest was the most successful classifier.

Abstract

With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this paper, the Latent Dirichlet Allocation, namely LDA, and Non-Negative Matrix Factorization methods in topic modeling were used to determine which emotion the Turkish tweets posted via Twitter. In addition, the accuracy of a proposed n-level method based on LDA was analyzed. Dataset consists of 5 emotions, namely angry, fear, happy, sad and confused. NMF was the most successful method among all topic modeling methods in this study. Then, the F1-measure of Random Forest, Naive Bayes and Support Vector Machine methods was analyzed by obtaining a file suitable for Weka by using the word weights and class labels of the topics. Among the Weka results, the most successful method was n-stage LDA, and the most successful algorithm was Random Forest.