Sentiment Analysis on Banking Feedback and News Data using Synonyms and Antonyms
control distance; datasets; ELECTRA; sentiment analysis; sentiment score; Synonyms and antonyms
Sentiment analysis is crucial for deciphering customers enthusiasm, frustration, and the market mood within the banking sector. This importance arises from financial datas specialized and sensitive nature, enabling a deeper understanding of customer sentiments. In todays digital and social marketing landscape within the banking and financial sector, sentiment analysis is significant in shaping customer insights, product development, brand reputation management, risk management, customer service improvement, fraud detection, market research, compliance regulations, etc. This paper introduces a novel approach to sentiment analysis in the banking sector, emphasizing integrating diverse text features to enable dynamic analysis. This proposed approach aims to assess the sentiment score of distinct words used within a document and classify them as positive, negative, or neutral. After rephrasing sentences using synonyms and antonyms of unique words, the system calculates sentence similarity using a distance control mechanism. Then, the system updates the dataset with the positive, negative, and neutral labels. Ultimately, the ELECTRA model utilizes the self-trained sentiment-scored data dictionary, and the newly created dataset is processed using the SoftMax activation function in combination with a customized ADAM optimizer. The approachs effectiveness is confirmed through the analysis of post-bank customer feedback and the phrase bank dataset, yielding accuracy scores of 92.15% and 93.47%, respectively. This study stands out due to its unique approach, which centers on evaluating customer satisfaction and market sentiment by utilizing sentiment scores of words and assessing sentence similarities. 2023, Science and Information Organization. All rights reserved.
International Journal of Advanced Computer Science and Applications, Vol-14, No. 12, pp. 934-944.
https://doi.org/10.14569/IJACSA.2023.0141294 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183092319&doi=10.14569%2fIJACSA.2023.0141294&partnerID=40&md5=dac49980a421a56d37bb8e0345f9965f
Mohanty A., Department of Computer Science and Engineering CHRIST (DEEMED TO BE UNIVERSITY), Bangalore, 560074, India; Cherukuri R.C., Department of Computer Science and Engineering CHRIST (DEEMED TO BE UNIVERSITY), Bangalore, 560074, India