Implementation of Text Mining System Development: A Case Study in Telecommunication Industry
DOI:
https://doi.org/10.32734/jsti.v24i1.7526Keywords:
Big Data, Text Mining, Sentiment Analysis, Naïve Bayes ClassifierAbstract
In today's internet age along with the advancement of web technology and its growth, large amounts of data today can be used and utilized to analyze and win the market. Social networking sites such as Twitter are quickly able to get neutral, positive and negative customer opinions and discussions using sentiment analysis methods. In this study the process of classifying sentiments using the Naïve Bayes Classifier method. This method approach provides digital-based surveys and comparative analysis such as machine learning and lexicon-based approaches. The results obtained from research based on data mining surveys there are five main complaints that are the main focus of telecommunications service companies, namely network, convenience, price, internet and services. Sentiment analysis based on test results showed the highest polarity of opinion was in the network at 60.07% and the lowest comfort with a value of 36.63%. The higher the polarity value, the higher the company's chances of making further improvements. The results showed that the development of big data mining systems helped companies to more accurately and effectively meet customer needs and classify sentiment towards products that have been launched.
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