Semantic Approach To Live Data Mining Using Clustering Technique
B Sophia, R. Rajaraman, S Mathan, Girja Shankar · 2021
In recent times, Social Networks became ubiquitous in our daily life. These networks can be used to obtain high-volume data related to any given event or topic. Twitter, one of the famous networks, became the wealthy supply of discussed topics. The users on Twitter express their point of views or opinions by tweets concerning different topics in a variety of fields. The data generated on Twitter can be extracted via Sentiment Analysis. In this proposed system, an artificial intelligence concept (NLP) is applied that understands the sentiment of extracted data and polarizes them into positive, negative, or neutral. These polarized data are then grouped based on their similarities and differences through a grouping algorithm. The data obtained is then represented graphically in form of a bar-chart. The information obtained from this text, an analysis based on emotions is used to create daily trends related to the event which is used to identify emerging patterns of this data or the current user sentiments related to the given event or topic.