Abstract
References
- [1] T. P. Sahu and S. Ahuja, “Sentiment analysis of movie reviews: A study on feature selection and classification algorithms,” Int. Conf. Microelectron. Comput. Commun. MicroCom 2016, 2016, doi: 10.1109/MicroCom.2016.7522583.
- [2] A. Hassan and A. Mahmood, “Deep Learning approach for sentiment analysis of short texts,” 2017 3rd Int. Conf. Control. Autom. Robot. ICCAR 2017, pp. 705–710, 2017, doi: 10.1109/ICCAR.2017.7942788.
- [3] S. Liao, J. Wang, R. Yu, K. Sato, and Z. Cheng, “CNN for situations understanding based on sentiment analysis of twitter data,” Procedia Comput. Sci., vol. 111, no. 2015, pp. 376–381, 2017, doi: 10.1016/j.procs.2017.06.037.
- [4] J. R. Quınlan, “Programs for machine learning. Part II,” in Machine Learning, vol. 7, pp. 135–240, 1994. [5] J. D. Bodapati, N. Veeranjaneyulu, and S. Shaik, “Ingenierie des Systemes d ’ Information Sentiment Analysis from Movie Reviews Using LSTMs,” vol. 24, no. 1, pp. 125–129, 2019.
- [6] T. Mikolov, K. Chen, G. S. Corrado, and J. A. Dean, “Computing numeric representations of words in a high-dimensional space,” 2015.
- [7] C. CORTES and V. VAPNIK, “Support-Vector Networks,” Mach. Leaming, vol. 20, no. 3, pp. 273–297, 1995, doi: 10.1109/64.163674.
- [8] Y. Y. Liu, M. Yang, M. Ramsay, X. S. Li, and J. W. Coid, “A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending,” J. Quant. Criminol., vol. 27, no. 4, pp. 547–573, 2011, doi: 10.1007/s10940-011-9137-7.
Abstract
Twitter sentiment classification is an artificial approach for examining textual information and figuring out what people's publicly tweets from a variety of industries are experiencing or thinking. For instance, a large number of tweets containing hashtags are posted online every minute from one user to some other user in the commercial and politics fields. It can be challenging for scientists to correctly comprehend the context in which specific tweet terms are used, necessitating a challenge in determining what is actually a positive or negative comment from the vast database of twitter data. The system's authenticity is violated by this issue and user dependability may be significantly diminished. In this study, twitter data sent to interpret movies were classified using various classifier and feature methods. In this context, the IMDB database consisting of 50000 movie reviews was used. For the purpose of anticipating the sentimental tweets for categorization, a huge proportion of twitter data is analyzed. In the proposed method, bag of words and word2vec methods are given by combining them instead of giving them separately to the classifier. With both the suggested technique, the system's effectiveness is increased and the data that are empirically obtained from the real world situation may be distinguished well. With experimental efficiency of 90%, the suggested approach algorithms' output attempts to assess the reviews tweets as well as be able to recognize movie reviews.
References
- [1] T. P. Sahu and S. Ahuja, “Sentiment analysis of movie reviews: A study on feature selection and classification algorithms,” Int. Conf. Microelectron. Comput. Commun. MicroCom 2016, 2016, doi: 10.1109/MicroCom.2016.7522583.
- [2] A. Hassan and A. Mahmood, “Deep Learning approach for sentiment analysis of short texts,” 2017 3rd Int. Conf. Control. Autom. Robot. ICCAR 2017, pp. 705–710, 2017, doi: 10.1109/ICCAR.2017.7942788.
- [3] S. Liao, J. Wang, R. Yu, K. Sato, and Z. Cheng, “CNN for situations understanding based on sentiment analysis of twitter data,” Procedia Comput. Sci., vol. 111, no. 2015, pp. 376–381, 2017, doi: 10.1016/j.procs.2017.06.037.
- [4] J. R. Quınlan, “Programs for machine learning. Part II,” in Machine Learning, vol. 7, pp. 135–240, 1994. [5] J. D. Bodapati, N. Veeranjaneyulu, and S. Shaik, “Ingenierie des Systemes d ’ Information Sentiment Analysis from Movie Reviews Using LSTMs,” vol. 24, no. 1, pp. 125–129, 2019.
- [6] T. Mikolov, K. Chen, G. S. Corrado, and J. A. Dean, “Computing numeric representations of words in a high-dimensional space,” 2015.
- [7] C. CORTES and V. VAPNIK, “Support-Vector Networks,” Mach. Leaming, vol. 20, no. 3, pp. 273–297, 1995, doi: 10.1109/64.163674.
- [8] Y. Y. Liu, M. Yang, M. Ramsay, X. S. Li, and J. W. Coid, “A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending,” J. Quant. Criminol., vol. 27, no. 4, pp. 547–573, 2011, doi: 10.1007/s10940-011-9137-7.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Araştırma Makalesi |
| Authors | |
| Early Pub Date | June 27, 2023 |
| Publication Date | June 27, 2023 |
| Submission Date | January 5, 2023 |
| Acceptance Date | May 9, 2023 |
| Published in Issue | Year 2023 Volume: 12 Issue: 2 |
