  Since a tweet is limited to 140 characters, it is ambiguous and difficult for
traditional Natural Language Processing (NLP) tools to analyse. This research
presents KeyXtract which enhances the machine learning based Stanford CoreNLP
Part-of-Speech (POS) tagger with the Twitter model to extract essential
keywords from a tweet. The system was developed using rule-based parsers and
two corpora. The data for the research was obtained from a Twitter profile of a
telecommunication company. The system development consisted of two stages. At
the initial stage, a domain specific corpus was compiled after analysing the
tweets. The POS tagger extracted the Noun Phrases and Verb Phrases while the
parsers removed noise and extracted any other keywords missed by the POS
tagger. The system was evaluated using the Turing Test. After it was tested and
compared against Stanford CoreNLP, the second stage of the system was developed
addressing the shortcomings of the first stage. It was enhanced using Named
Entity Recognition and Lemmatization. The second stage was also tested using
the Turing test and its pass rate increased from 50.00% to 83.33%. The
performance of the final system output was measured using the F1 score.
Stanford CoreNLP with the Twitter model had an average F1 of 0.69 while the
improved system had a F1 of 0.77. The accuracy of the system could be improved
by using a complete domain specific corpus. Since the system used linguistic
features of a sentence, it could be applied to other NLP tools.
