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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. Natural Language processing Twitter comes up with grammar rules which are made in a way that they are only recognized by the Twitter compile. Such rules can be likened to the English grammar rules like parts of speech, punctuations and many others. There are two important aspects we need to look at when dealing with natural language processing since they try to compare the structure of English and how it can be tuned to be accepted in social media platforms. They are:- 1. Parts of Speech Tagging 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. The tagger examines a certain message from a user and tries to get the meaning of the sentence using English as a natural language. Unfortunately, most posts on social media are abbreviated and so if one is not conversant with the abbreviations used, they may miss out on the intended meaning. Tagger uses previous posts as training data and learns the meaning of abbreviations and short form that are used on twitter. Having learned and gained experience, when future posts come in for processing, the tagger can easily give out the meaning of the post and depending on how it’s programmed, it may advise whether it’s a bad or good message. 2. TweeboParser. 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. Importance of analyzing sentiments It is very important that analysis of the tweets is done. This is beneficial to the government of any given country. The governmental security agencies use twitters sentiment analysis to determine what its citizens are talking regarding the government. Is a matter of fact, it helps the government in censoring people with malicious intentions who want to compromise security of a given country. 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.
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Natural Language Processing Twitter, twitter sentiment analysis,
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