X (FORMALLY TWITTER) PRODUCT CLASSIFICATION USING NAÏVE BASE

Authors

Keywords:

Polarities, Textual, Sentiment, Vectorizing, Unique

Abstract

Finding polarities in a textual data is very important in sentiment analysis. Navie Bayes is one of the most effective machine learning classifier techniques and a probabilistic classier that applies Bayes theorem and assuming feature independence for the classification of data. The objective of this research is to implement the Naïve Bayes algorithm for the classification of sentiment in the context of X (Formally Twitter) product classification. Our aim is developing a model that can conveniently classify product-related text into positive and negative sentiment categories. The process begins with the collection of customer product reviews from X (Formally Twitter) and vectorizing each reviews making a long array of unique words, whose attributes include the independent vectors while assigned values are the number of times each independent vector appear in the product review. We had a total of one hundred and thirty-three (133) unique words in the training set for both the positive and Negative statements of which ten (10) documents were with positive (+) outcomes and ten (10) documents were with negative (-) outcomes. From the product reviews we derived the probability of each positive outcomes of independent word, probability of each negative outcomes of independent words and then the probability of each word in the product review. Our results shows that the naïve bayes classifier is a good classification technique and will be effective for both large and small businesses in making decisions related to their product development, marketing campaigns and customer support.

Dimensions

Abinaya, R., Aishwaryaa, P., Baavana, S., and Selvi, N. T. (2016, July). Automatic sentiment analysis of user reviews. In 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 158-162. IEEE.

Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R. J. (2011). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media (LSM 2011), 30-38.

Dey, L., Chakraborty, S., Biswas, A., Bose, B., and Tiwari, S. (2016). Sentiment analysis of review datasets using naive bayes and k-nn classifier. International Journal of Information Engineering and Electronic Business (IJIEEB), 8(4), 54-62, https://doi.org/10.5815/ijieeb.2016.04.07

Fang, X., and Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big data, 2, 1-14.

Hemalatha, I., Varma, G. P. S., and Govardhan, A. (2014). Automated sentiment analysis system using machine learning algorithms. IJRCCT, 3(3), 300-303.

Korde, V., and Mahender, C. N. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence and Applications, 3(2), 85.

Liu, Bing. (2012). Sentiment analysis and opinion mining, Morgan and Claypool publishers, May 2012.

Mohan, V., and Venu, S. H. (2016). Sentiment analysis applied to airline feedback to boost customers Endearment. International Journal of Applied and Physical Sciences, 2(2), 51-58

Narayanan, V., Arora, I., and Bhatia, A. (2013). Fast and accurate sentiment classification using an enhanced Naive Bayes model. In Intelligent Data Engineering and Automated LearningIDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings 14, 194-201, Springer Berlin Heidelberg.

Orie C.E, Egwali A.O. Amadin F. I. (2023). Machine Learning Based Sentiment of Tweet Data. 5th Annual Conference of the Society for Forensic and Analytical Scientist Nigeria.

Pang, B., and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in information retrieval, 2(12), 1-135.

Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques, Proc. 2002 Conf. on Empirical Methods in Natural Language Processing (EMNLP).

Prof. (Mr.) Prashant Kanade, Lakhan Rangwani, Pritee Wadhwa, Jitesh Watwani and Nitin Hazarani (2019). Automatic Sentiment Analysis of User Reviews, International Journal of Information and Computation Technology, 9(1), 5-10

Rai, A., and Jaiswal, K. (2017). Sentiment analysis by using unsupervised comment summarization. International Research Journal of Engineering and Technology, 4(5), 1105-1109.

Ramdhani, S. L., Andreswari, R., and Hasibuan, M. A. (2018, November). Sentiment analysis of product reviews using naive bayes algorithm: A case study. In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 123-127, IEEE.

Shah, A. (2021). Sentiment analysis of product reviews using supervised learning. Reliability: Theory and Applications, 16(1), 243-253.

Shukla, R., and Mishra, N. (2016). Framework for Sentiment Analysis of Twitter Post. International Journal of Innovative Research in Science, Engineering and Technology, 5(3).

Singla, Z., Randhawa, S., and Jain, S. (2017). Sentiment analysis of customer product reviews using machine learning. In 2017 international conference on intelligent computing and control (I2C2), 1-5, IEEE.

Suchdev, R., Kotkar, P., Ravindran, R., and Swamy, S. (2014). Twitter sentiment analysis using machine learning and knowledge-based approach. International Journal of Computer Applications, 103(4).

Umarani, V., Julian, A. and Deepa, J. (2021). Sentiment Analysis using various Machine Learning and Deep Learning Techniques. Journal of the Nigerian Society of Physical Sciences. Volume 3, issue 4, pp 385-394.

Vidhya, K. A., and Aghila, G. (2010, February). Hybrid text mining model for document classification. In 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 1, 210-214, IEEE.

Published

31-01-2025

How to Cite

X (FORMALLY TWITTER) PRODUCT CLASSIFICATION USING NAÏVE BASE. (2025). FUDMA JOURNAL OF SCIENCES, 9(1), 46-55. https://doi.org/10.33003/fjs-2025-0901-2795

Issue

Vol. 9 No. 1 (2025): FUDMA Journal of Sciences - Vol. 9 No. 1

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

X (FORMALLY TWITTER) PRODUCT CLASSIFICATION USING NAÏVE BASE. (2025). FUDMA JOURNAL OF SCIENCES, 9(1), 46-55. https://doi.org/10.33003/fjs-2025-0901-2795