Processing of Natural Language has become popular in TwitterPosted by articlelink01 on July 25th, 2015 Natural language refers to any language that has developed in human being’s brains without any conscious planning or premeditation of their own. In the world today, we have a very large number of natural languages that exist. Among this many languages, we have few that have conventionally been accepted to be used in technological devices. English has become a language that is used by many people in the world; hence almost if not all technology devices are programmed to work with English language.
This provides a fast and robust java-based tokenizer for tweets analysis. It applies the principles of Machine learning such that it has some prior data that has both the input and the output in different situations. This initial knowledge is what we refer to as training data. It is from this Training data that the system learns and gains experience that is useful in making decisions of that type in future. Here, the tagger performs a very crucial role in learning from the training data that may be the conversations from the users of that particular social media, mostly twitter.
This provides a dependency parser for English tweets. Since most of the texts in twitter have no syntactic structure, the Tweeboparser tries to predict their syntax structure which is represented by unlabeled dependencies. Mostly, Tweeboparser output looks like a cluster of graphs, this is because a tweet may contain more than one utterances. Generally, Natural Language Processing Twitter is what is being used today after barcodes and mnemonic codes that were used in systems before. It has made processing easier since words used are common English words. The processing helps decode the meanings of different tweets hence making twitter sentiment analysis easier. Like it? Share it!More by this author |