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
Date
17-11-2020
Document Type
Original article
Recommended Citation
Hayder, Wrya Anwar
(2020)
"Supervised Sentiment Analysis Model of Textual Content for Images,"
Passer Journal: Vol. 2
:
Iss.
2
, Article 6.
DOI: 10.24271/psr.16
Available at:
https://passer.garmian.edu.krd/journal/vol2/iss2/6
