Sentimental Analysis Using Association Rule Mining-A Survey - S.Ashika Parvin, M.Sumathi (International Journal of Advanced Science and Technology, 2020)
Today’s word is called an “era of information” or it can be called as “an era of immensely colossal data” because there is an exponential magnification of information being flooded online, so called “big data”. This lead to the desideratum for mining the data. Data mining (DM) is the process used to extract utilizable data from a more immensely colossal set of any raw data. An incipient definition can be given to DM as “mining the data from the big data for one’s own purpose”. The exponential magnification of available online information paves a way for the computer engineers with many incipient challenges and opportunities. A recent trend is to analyze people feelings and opinions about frequently sold products and brands that are available online. Sentiment Analysis (SA) is the computational treatment of opinions, sentiments and subjectivity of text about the frequent item sets which is investigated utilizing association rule mining. This survey paper tackles a comprehensive overview of the last update in these two fields. The main target of this survey is to give proximately full image of association rule mining and SA techniques and its cognate fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in these two areas.