A EXPLORATORY ANALYSIS OF PROCESSING FREQUENCY WORD DENSITY ALONG WITH NAMED ENTITY RECOGONITION

ABSTRACT
Text analytics is the most popular form in our day to day conversion. Most of the data which we generate is unstructured and leads into processing the generate insights in nature. Natural language processing enables the human to interact with systems in a natural way. Named Entity Recognition (NER) is task of extracting information from unstructured text which can be categorized by persons, locations, organizations, cost values, percentages, expressions and so forth. This paper describes the processing of the twitter positive sentiment tweets and negative sentiment tweets, text corpus. The tweets and web text corpus data noises are pre processed, frequency density of words are analyzed from tweets and web text corpus data by following with Named Entity Recognition(NER) chunked tree for twitter corpus and web text corpus.
Keywords Frequency Density, Tweets, Sentiments, Named Entity Recognition