@article { author = {Hayder, Wrya}, title = {Supervised Sentiment Analysis Model of Textual Content for Images}, journal = {Passer Journal of Basic and Applied Sciences}, volume = {2}, number = {2}, pages = {81-86}, year = {2020}, publisher = {University of Garmian}, issn = {27065944}, eissn = {27065952}, doi = {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}, url = {https://passer.garmian.edu.krd/article_133258.html}, eprint = {} }