Sentiment analysis using machine learning techniques


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).


Данные скачивания пока не доступны.

Биография автора

Somar Bilal, Don State Technical University

PhD student second year, Don State Technical University


1. Mandloi L. and Patel R. (2020) Twitter sentiments analysis using machine learninig methods. Int. Conf. Emerg. Technol. INCET 2020. P. 1–5. DOI
2. Prakruthi V., Sindhu D. and Anupama Kumar S. (2018) Real Time Sentiment Analysis of Twitter Posts. Proc. 2018 3rd Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. CSITSS 2018. P. 29–34. DOI
3. Wongkar M. and Angdresey A. (2019) Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter. Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019. P. 1–5. DOI
4. ParkC. W. and Seo D. R. (2018) Sentiment analysis of Twitter corpus related to artificial intelligence assistants. 2018 5th Int. Conf. Ind. Eng. Appl. ICIEA 2018. P. 495–498. DOI
5. Dhawan S., Singh K. and Chauhan P. (2019) Sentiment Analysis of Twitter Data in Online Social Network. Proc. IEEE Int. Conf. Signal Process. Control. Vol. 2019-Octob, P. 255–259. DOI
6. Kusrini and Mashuri M. (2019) Sentiment analysis in twitter using lexicon based and polarity multiplication. Proceeding – 2019 Int. Conf. Artif. Intell. Inf. Technol. ICAIIT 2019. P. 365–368. DOI
7. Wagh R. and Punde P. (2018) Survey on Sentiment Analysis using Twitter Dataset. Proc. 2nd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2018, no. Iceca. P. 208–211, 2018, DOI
8. Permatasari R. I., Fauzi M. A., Adikara P. P. and Sari E. D. L. (2018) Twitter Sentiment Analysis of Movie Reviews using Ensemble Features Based Naïve Bayes. 3rd Int. Conf. Sustain. Inf. Eng. Technol. SIET 2018 – Proc. P. 92–95. DOI
9. Parveen H. and Pandey S. (2017) Sentiment analysis on Twitter Data-set using Naive Bayes algorithm. Proc. 2016 2nd Int. Conf. Appl. Theor. Comput. Commun. Technol. iCATccT 2016. P. 416– 419. DOI
10. Shelar A. and Huang C. Y. (2018) Sentiment analysis of twitter data. Proc. – 2018 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2018. P. 1301– 1302. DOI
12. Ghosh K., Banerjee A., Chatterjee S. and Sen S. (2019) Imbalanced Twitter Sentiment Analysis using Minority Oversampling. 2019 IEEE 10th Int. Conf. Aware. Sci. Technol. iCAST 2019 – Proc. P. 1–5. DOI
13. Tiwari S., Verma A., Garg P. and Bansal D. (2020) Social Media Sentiment Analysis on Twitter Datasets. 2020 6th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2020. P. 925–927. DOI
14. Ray P. and Chakrabarti A. (2017) Twitter sentiment analysis for product review using lexicon method. 2017 Int. Conf. Data Manag. Anal. Innov. ICDMAI 2017. P. 211–216. doi: 10.1109/ICDMAI.2017.8073512" target="_blank">DOI
15. Imamah and Rachman F. H. (2020) Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion. Proceeding – 6th Inf. Technol. Int. Semin. ITIS 2020. P. 238– 242. DOI
16. Kariya C. and Khodke P. (2020) Twitter sentiment analysis. 2020 Int. Conf. Emerg. Technol. INCET 2020. P. 7–9. DOI
Как цитировать
Bilal, S. (2023). Sentiment analysis using machine learning techniques. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (1), 106-113.
Интеллектуальные системы, анализ данных и машинное обучение