Sentiment analysis using machine learning techniques

Авторы

DOI:

https://doi.org/10.17308/sait/1995-5499/2023/1/106-113

Ключевые слова:

stochastic gradient descent classifier, Naive Bayes algorithm, sentiment analysis, twitter, natural language processing, text mining, opinion mining, social media

Аннотация

Sentiment analysis is a viral activity in recent years, especially with the spread of social media. It is a way to analyze people’s opinions of the products and services offered by many companies. It is also a way to understand the political, artistic and sports trends of the world. In this paper, we are going to study sentiment analysis and natural language processing. we will create a machine learning model to analyze many Twitter tweets to predict the sentiment of people. AI and machine learning-based sentiment analysis is crucial for companies because it enabled these companies to automatically predict whether their customers are happy or not. This research is important and directly applicable to pretty much any company that has an online presence such as Twitter or Facebook pages. The algorithms could be used to automatically detect and possibly hate or racist tweets as well. Many methods were used to classify the data into positive and negative. This study was applied to a data set taken from Kaggle, where the accuracy of the training data reached 97 % using a stochastic gradient descent Classifier (SGDClassifier).

Биография автора

  • Somar Bilal, учреждение высшего образования Донской государственный технический университет

    PhD student second year, Don State Technical University

Библиографические ссылки

Загрузки

Опубликован

2023-05-12

Выпуск

Раздел

Интеллектуальные системы, анализ данных и машинное обучение

Как цитировать

Sentiment analysis using machine learning techniques. (2023). Вестник ВГУ. Серия: Системный анализ и информационные технологии, 1, 106-113. https://doi.org/10.17308/sait/1995-5499/2023/1/106-113