ABSTRACT
The uses of social media platform twitter have progressed to a much wider array of uses due to the number of data it processes every day. It has been a storage for unstructured data that could be useful once arranged into meaningful formats and can produce helpful means to identify and categorize uploaded tweets real time. A Filipino Tweets Categorization system that categorizes tweet inputs into 4 different categories such as Politics, Entertainment, Sports and Technology was developed. Different data processing methods were used to Upon evaluation, the system was found to have effective results in terms of accuracy. As for the recommendations, additional data may be added as well as new categories to further improve the system's functionalities.
- Alexen A. Elacio; Luisito L. Lacatan; Albert A. Vinluan; Francis G. Balazon, “Machine Learning Integration of Herzberg's Theory using C4.5 Algorithm ,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 1.1, pp. 57–63, 2020, doi: 10.30534/ijatcse/2020/1191.12020.Google Scholar
- A. A. Elacio, F. G. Balazon, and L. L. Lacatan, “Digital Transformation in Managing Employee Retention using Agile and C4 . 5 Algorithm,” no. 15217, pp. 15217–15225, 2020.Google Scholar
- L. A. Plotitsyna, G. M. Kadyrova, A. N. Boyko, A. V. Zubenko, and A. M. Fedotov, “Mathematical and software for sentimental analysis of text data,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3207–3210, 2020, doi: 10.30534/ijatcse/2020/112932020.Google ScholarCross Ref
- L. L. Lacatan, “Hybrid Method and Face to Face Method in Teaching Mathematics: Effects on Students ’ Performance,” vol. 3, no. 2, pp. 143–146, 2013, doi: 10.7763/IJIET.2013.V3.252.Google Scholar
- G. M. Pangilinan, M. A. F. Quioc, and L. L. Lacatan, “Integrating Artificial Neural Network and Smartbot on the Development of an E-learning Platform,” no. 5716, 2020.Google Scholar
- L. Lacatan, “Success of Hybrid Method to an e-Community of Learners in Cyberspace,” Proc. Appl. Int. Bus. Conf. 2008 SHORT-TERM, pp. 381–384, 2008.Google Scholar
- L. A. Tolentino and A. Borra, “An Exhaustive Rule-Based Affix Extraction for Stemming in Tagalog.”Google Scholar
- J. Kaur and S. Bhagla, “News Classification Using Naïve Baye ’ s Classifier,” vol. 6, no. 4, pp. 698–702, 2016.Google Scholar
- F. R. Lapitan, R. T. Batista-Navarro, and E. Albacea, “Crowdsourcing-based Annotation of Emotions in {F}ilipino and {E}nglish Tweets,” Proc. 6th Work. South Southeast {A}sian Nat. Lang. Process., pp. 74–82, 2016.Google Scholar
- M. M. Pippin, R. J. C. Odasco, R. E. De Jesus, M. A. Tolentino, and R. P. Bringula, “Classifications of emotion expressed by filipinos through tweets,” Lect. Notes Eng. Comput. Sci., vol. 1, pp. 292–296, 2015.Google Scholar
- J. N. Mindoro, N. U. Pilueta, Y. D. Austria, L. Lolong Lacatan, and R. M. Dellosa, “Capturing Students’ Attention through Visible Behavior: A Prediction Utilizing YOLOv3 Approach,” 2020 11th IEEE Control Syst. Grad. Res. Colloquium, ICSGRC 2020 - Proc., no. August, pp. 328–333, 2020, doi: 10.1109/ICSGRC49013.2020.9232659.Google Scholar
- K. Lee, D. Palsetia, R. Narayanan, M. M. A. Patwary, A. Agrawal, and A. Choudhary, “Twitter trending topic classification,” Proc. - IEEE Int. Conf. Data Mining, ICDM, no. December, pp. 251–258, 2011, doi: 10.1109/ICDMW.2011.171.Google ScholarDigital Library
- A. B. Adetunji, J. P. Oguntoye, O. D. Fenwa, and N. O. Akande, “Web Document Classification Using Naïve Bayes,” J. Adv. Math. Comput. Sci., vol. 29, no. 6, pp. 1–11, 2018, doi: 10.9734/jamcs/2018/34128.Google ScholarCross Ref
- J. C. Alejandrino, A. J. P. Delima, and R. N. Vilchez, “It students selection and admission analysis using naïve bayes and c4.5 algorithm,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 1, pp. 759–765, 2020, doi: 10.30534/ijatcse/2020/108912020.Google ScholarCross Ref
- J. C. Tesoro, M. J. M. Buen, R. C. Sullera, and M. V. Aborde, “A semantic approach of the naïve bayes classification algorithm,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3287–3294, 2020, doi: 10.30534/ijatcse/2020/125932020.Google ScholarCross Ref
- B. Shruti, And, and G. Vishal, “Text News Classification System using Naïve Bayes Classifier,” Vishal Goyal Res. Cell An Int. J. Eng. Sci., vol. 3, no. December, pp. 209–213, 2014.Google Scholar
- D. Buzic and J. Dobsa, “Lyrics classification using Naive Bayes,” 2018 41st Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2018 - Proc., no. June, pp. 1011–1015, 2018, doi: 10.23919/MIPRO.2018.8400185.Google ScholarCross Ref
Index Terms
- Text Categorization of Filipino Tweets Using Naïve Byes Algorithm
Recommendations
Sentence-Level Sarcasm Detection in English and Filipino Tweets
ICIBE '18: Proceedings of the 4th International Conference on Industrial and Business EngineeringSarcasm is a special form of sentiment which defines as "a nuanced form of language in which individuals say the opposite of what is implied". In this study, the researchers collected 6,000 Tagalog tweets and 6,000 English tweets from the microblogging ...
Automatic Arabic document categorization based on the Naïve Bayes algorithm
Semitic '04: Proceedings of the Workshop on Computational Approaches to Arabic Script-based LanguagesThis paper deals with automatic classification of Arabic web documents. Such a classification is very useful for affording directory search functionality, which has been used by many web portals and search engines to cope with an ever-increasing number ...
Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values
Each type of classifier has its own advantages as well as certain shortcomings. In this paper, we take the advantages of the associative classifier and the Naive Bayes Classifier to make up the shortcomings of each other, thus improving the accuracy of ...
Comments