How Twitter Has Capabilities Of Processing Natural Language Impressively

In the world today, we have a very large number of natural languages that exist. English is the only language that is conventionally known to be used in most of the countries in the world, therefore most digital appliances and devices have been coded using high level language which is almost the same as natural language.

Grammar rules are made in a way that they are only recognized by the Twitter compile. We can try to compare the rules used in twitter data processing with those used in English Grammar. English has various rules governing it for instance we have rules on punctuations, parts of speech and many others. Below is a tool that twitter uses to process natural languages, especially English which is used in almost all countries in the world:-

Parts of Speech Tagging

This is a tool in natural language processing twitter that provides a fast and robust java-based tokenize for tweets analysis. It deploys the principles of linear regression such that it has some prior data that has both the input and the output in different situations.

The tagger looks at 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 been experienced, if a message is posted in future for processing, the tagger easily gives out the meaning of the post and depending on how it’s programmed, it may categorize it as being hate speech, a joke or merely a simple a statement that has no basis.

Data analysis made easier

It’s now possible to do Social data analysis which refers to checking through the data of a given platform and applying the necessary analysis techniques in order to draw conclusions. It has been made possible through a number of ways. The first way is through benchmarking the competitors in order to get the insights and media performance with best practice examples. Secondly, with quaintly, social data analysis has been made very much possible and so we no longer need to waste our quality time figuring out how we can track the social media KPIs.

In addition, this data analysis has been centralized such that all the data from different social media can now be combined in one tool and visualized and checked for marketing success. The reporting system is equally amazing as it can be automated to set up white labeled templates and sent as one likes.