A Review of Turkish Sentiment Analysis and Opinion Mining

Abstract

Social Media is one of the most frequently used platforms today. Users can easily share their views, ideas, and thoughts on this platform. The data shared on social media platforms is actually a great deal that can be transformed into meaningful information. The obtained big data can be analyzed and evaluated by various data analysis methods. Whether or not the data contain a feeling, if it is included; the type of the feeling (i.e. positive, negative or neutral) can be determined by emotion analysis methods. Sentiment Analysis studies in later times began to turn to analysis indicating different sentiments. Thus the foundations of Opinion Mining were laid. When ideas conveyed by social media information are presented semantically, they are expressed by Opinion Mining. The purpose of this paper is to explain the relationship between the concepts of Sentiment Analysis and Opinion Mining. The terms used in Sentiment Analysis and Opinion Mining are explained and examples of Turkish Sentiment Analysis are given. It has been tried to suggest solutions for the problems encountered in Turkish studies.

Tweet Classification and Sentiment Analysis on Metaverse Related Messages

“…The terms used for these two concepts are explained and examples are given for Turkish sentiment analysis. In addition, the problems encountered while carrying out Turkish studies were examined and solutions were offered for these problems [7]. Abali et al conducted a study using Turkish tweets from the Aegean Region of Turkey.…”

High Performance Twitter Sentiment Analysis Using CUDA Based Distance Kernel on GPUs

“…Specific characteristics of Turkish make it difficult to perform Natural Language Processing (NLP) tasks (e.g., Named Entity Recognition, Sentiment Analysis, Word Sense Disambiguation etc.) as it requires effective pre-processing and increases the feature space further [4], [5]. Structural processing in Turkish is more difficult than in English and performing sentiment analysis for Turkish is a challenging task due to its rich morphology and abundance of dialectal use in Twitter [6].…”