A COMPARISON STUDY ON TEXT MINING AND SENTIMENT ANALYSIS FEATURES AND FUNCTIONS USING SAS ENTERPRISE MINER, PYTHON AND R

Abstract

Twitter has allowed textual data to be collected using Text Mining and Sentiment Analysis techniques in the age of social media in which user-generated content becomes redundant. However due to some inconsistencies, Text Cleaning plays an important role before Text Mining and Sentiment Analysis techniques can be conducted. Hence, this study is conducted to discover the capabilities of Text Cleaning, Text Mining and Sentiment Analysis in three different data mining tools: SAS® Text Miner (proprietary text mining tool), Python and R programming (open-source text mining tools). These data mining tools were used to conduct the Text Cleaning, Text Mining and Sentiment Analysis and their capabilities such as features, functions and characteristics were evaluated and looked into, in order to conduct this comparison study. All the proposed research objectives were met successfully even with the given limitation. A movie critique Dictionary is one of the major theoretical implications of this research. Based on our analysis and results, developers or educational practitioners can discover what is important and what is unimportant when conducting Text Mining and Sentiment Analysis. They will also obtain insights and guidance on how to conduct Text Mining and Sentiment Analysis using SAS Enterprise Miner, Python and R.