Supervised Sentiment Analysis Model of Textual Content for Images

Hayder, Wrya Anwar

doi:10.24271/psr.16

Supervised Sentiment Analysis Model of Textual Content for Images

Document Type : Original Article

Author

Wrya Anwar Hayder

Department of IT , College of Computer & IT, University of Garmian, Kalar, Kurdistan Region, Iraq

10.24271/psr.16

Abstract

Sentiment analysis is a domain in machine learning that tries to analyze people’s emotion, feeling, opinion and attitudes towards particular service or product. It aims to extract feelings and opinion from textual reviews; therefore, it is closely related to natural language processing (NLP). Social media has provided a huge amount of text reviews, which is practically impossible to read and analyze the emotions, attitudes and opinions that were expressed in those textual data. Sentiment analysis is a machine learning concept to classify a textual data according to reviewers’ emotion and attitudes about a service or product, which helps in determine strong or weak production. In this paper work we aim to develop a sentiment analysis model of texts for images. Different machine learning algorithms are tested such as Naive Bays, Logistic Regression and Support Vector Machine (SVM), in order to develop a high accuracy sentiment analysis system. The model is developed to determine whether a text has positive or negative emotion for images. The outcome of the project work shows that SVM algorithm has a better performance for such purpose, while Logistic Regression algorithm shows a faster execution time.

Keywords

Machine learning

sentiment analysis

NLP model

Sentiment system

Machine learning model

Text mining