Machine Learning Based Sentiment Analysis of Message on Twitter
DOI:
https://doi.org/10.54097/hset.v38i.5980Keywords:
Machine learning; semantic analysis; neural network.Abstract
In the age of information explosion, it is a very important and challenging task to extract the required features from a huge amount of information. The emotion expressed by the text information is one of the most important features of the information. However, there is not much research in this field, so it is of great significance and exploratory to the text sentence emotion analysis. In order to compare and explore better feasibility, both the sequential neural network model and the random forest model were built. Through the contrast between the two models, the machine learning of datasets composed of Twitter comments is carried out to analyze the emotion of the text, and emotion extraction is the research topic of this paper. In this paper, emotion analysis was studied in the order of data processing, model building, and result analysis, and an accuracy of about 90% was finally achieved, which is a good result, from which it can be seen that the constructed neural network model plays a good role.
Downloads
References
Wang X, Kou L, Sugumaran V, et al. Emotion correlation mining through deep learning models on natural language text. IEEE transactions on cybernetics, 2020, 51(9): 4400-4413.
Gautam G, Yadav D. Sentiment analysis of twitter data using machine learning approaches and semantic analysis. 2014 Seventh international conference on contemporary computing (IC3). IEEE, 2014: 437-442.
Cambria E, Das D, Bandyopadhyay S, et al. Affective computing and sentiment analysis. A practical guide to sentiment analysis. Springer, Cham, 2017: 1-10.
Cambria E, Poria S, Hussain A, et al. Computational intelligence for affective computing and sentiment analysis. IEEE Computational Intelligence Magazine, 2019, 14(2): 16-17.
Mejova Y. Sentiment analysis: An overview. University of Iowa, Computer Science Department, 2009.
Habimana O, Li Y, Li R, et al. Sentiment analysis using deep learning approaches: an overview. Science China Information Sciences, 2020, 63(1): 1-36.
Karamibekr M, Ghorbani A A. Lexical-syntactical patterns for subjectivity analysis of social issues. International Conference on Active Media Technology. Springer, Cham, 2013: 241-250.
Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260.
Bollen J, Pepe A, Mao H. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. arXiv preprint arXiv:0911.1583, 2009.
Maas A, Daly R E, Pham P T, et al. Learning word vectors for sentiment analysis. Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies. 2011: 142-150.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
