Abstract:
Part-of-Speech Tagging is the technology of assigning the appropriate parts-of-speech to a word. Part-of-speech
tagging is very useful in information retrieval, information extraction, and speech processing. This research presents a
part-of-speech tagging, especially for twitter text data. The process of part-of-speech tagging for twitter conversation is
a difficult task. Several approaches have been made to develop an accurate tagging system but most of them are relevant
to news text data and web contents. Therefore, this research intends to develop a part-of-speech tagger model for twitter
speech. using CRF toolkit. The system was developed for nearly 1000 twitter conversations employing Conditional
Random Field stochastic model. The data for twitter speech was downloaded from the internet. A POS-tagged text
corpus, template file and CoNLL file for both training and testing database were prepared accordingly. The training was
carried out for both the unigram model as well as the bigram model. The performance of the system over these models
was obtained through this examination which showed a significant efficiency, calculated from the number of correctly tagged words and the total number of words
