Julio Jeremy Escudero Macrohon
ABSTRACT
Social media has emerged as a significant source of big data, offering a vast amount of social data and evidence. However, much of this data remains untapped and underutilized. Nowadays, various organizations, including corporations, government agencies, and data enthusiasts, are actively seeking to extract and leverage this data to create data models that drive actionable insights, helping them achieve their goals. This study focuses on examining customer sentiments towards Bank A, one of the largest banking and financial institutions in the Philippines with a history spanning over 50 years. By employing natural language processing techniques, we aimed to gain a comprehensive understanding of the general sentiment towards the bank's products, services, and overall brand satisfaction. To accomplish this, we annotated the sentiments and utilized them as training and testing data for a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). This model was designed to classify tweets in both English and Filipino languages into two polarities and demonstrated an accuracy rate of 88.50%.
- X. Zhao and Y. Ohsawa, "Sentiment Analysis on the Online Reviews Based on Hidden Markov Model," Journal of Advances in Information Technology, vol. 9, no. 2, pp. 33-38, 2018.Google Scholar
Cross Ref
- M. A. Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.Google Scholar
- H. K. Darshan, A. R. Shankar, B. S. Harish and K. K. H. M, "Exploiting RLPI for Sentiment Analysis on Movie Reviews," Journal of Advances in Information Technology, vol. 10, no. 1, pp. 14-19, 2019.Google ScholarCross Ref
- I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016.Google ScholarDigital Library
- M. Darwich, S. A. M. Noah, N. Omar, N. A. Osman and I. S. Ahmad, "Quantifying the Natural Sentiment Strength of Polar Term Senses Using Semantic Gloss Information and Degree Adverbs," Journal of Advances in Information Technology, vol. 11, no. 3, pp. 109-118, 2020.Google ScholarCross Ref
- A. Hamouda, M. Marei and M. Rohaim, "Building Machine Learning Based Senti-word Lexicon for Sentiment Analysis," Journal of Advances in Information Technology, vol. 2, no. 4, pp. 199-203, 2011.Google ScholarCross Ref
- B. Pang and L. Lee, "Opinion Mining and Sentiment Analysis," Foundations and Trends® in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.Google ScholarDigital Library
- R. Atienza and R. Mina, "Sentiment Analysis of Filipino Tweets Using Recurrent Neural Networks with Long Short-Term Memory," in 2019 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2019.Google Scholar
- J. Rennie, "Improving multiclass text classification with Naïve Bayes," in Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, 2001.Google Scholar
- M. Pippin Jr., "Evaluating the effectiveness of Naïve Bayes and SVM for sentiment analysis on Twitter," in Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC), 2017.Google Scholar
- G. D. Patacsil, "Sentiment Analysis of Customer Reviews of Filipino Internet Service Providers Using Naïve Bayes Technique," in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018.Google Scholar
- C. Villavicencio, J. Macrohon, X. Inbaraj, J. Jeng and J. Hsieh, "Twitter sentiment analysis towards COVID-19 vaccines in the Philippines using Naïve Bayes," Information, vol. 12, no. 5, p. 204, 2021.Google ScholarCross Ref
- D. Elangovan and V. Subedha, "Firefly with Levy Based Feature Selection with Multilayer Perceptron for Sentiment Analysis," Journal of Advances in Information Technology, vol. 14, no. 2, pp. 342-349, 2023.Google ScholarCross Ref
- S. Velampalli, C. Muniyappa and A. Saxena, "Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models," Journal of Advances in Information Technology, vol. 13, no. 2, pp. 167-172, 2022.Google ScholarCross Ref
- S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.Google ScholarDigital Library
- C. Olah, "Understanding LSTM Networks," 2015. [Online]. Available: http://colah.github.io/posts/2015-08-Understanding-LSTMs/.Google Scholar
Index Terms
- Sentiment Analysis using a Long Short-Term Memory Recurrent Neural Network on Filipino Tweets: A Case Study for Bank A
Recommendations
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge managementTwitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various ...
Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network
ICCIP '17: Proceedings of the 3rd International Conference on Communication and Information ProcessingOne biggest challenge in sentiment analysis is that it should include Natural Language Processing (NLP), to make the machine understand the human language. With the current development of Artificial Neural Network (ANN), with its implementation, ...
Sentiment Analysis in the Light of LSTM Recurrent Neural Networks
Long short-term memory LSTM is a special type of recurrent neural network RNN architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more accurately. In this article, the authors work with ...
Comments