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Since a tweet is limited to 140 characters, it is ambiguous and difficult fortraditional Natural Language Processing (NLP) tools to analyse. This researchpresents KeyXtract which enhances the machine learning based Stanford CoreNLPPart-of-Speech (POS) tagger with the Twitter model to extract essentialkeywords from a tweet. The system was developed using rule-based parsers andtwo corpora. The data for the research was obtained from a Twitter profile of atelecommunication company. The system development consisted of two stages. Atthe initial stage, a domain specific corpus was compiled after analysing thetweets. The POS tagger extracted the Noun Phrases and Verb Phrases while theparsers removed noise and extracted any other keywords missed by the POStagger. The system was evaluated using the Turing Test. After it was tested andcompared against Stanford CoreNLP, the second stage of the system was developedaddressing the shortcomings of the first stage. It was enhanced using NamedEntity Recognition and Lemmatization. The second stage was also tested usingthe Turing test and its pass rate increased from 50.00% to 83.33%. Theperformance 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 theimproved system had a F1 of 0.77. The accuracy of the system could be improvedby using a complete domain specific corpus. Since the system used linguisticfeatures of a sentence, it could be applied to other NLP tools.

KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools