Big data Analytics using Natural Language Processing algorithms to process Social media data and present it with a simple visualization platform to the customers.
It’s said, Simple algorithm with big data is better than complex algorithm with small data??. Computers understand structures languages known as coding. It’s ironic that natural language which is mostly unstructured is easiest for humans to learn and use, is toughest for a computer to master it.
In case of language used in social media, the data used is not only huge, but lots of abbreviations are also used such as lol, ASAP, etc. Apart from it, in language used here, is human language; it contains different words to be interpreted by the computer, along with lots of emotions, sentiments, as well as altered meaning. Further, the biggest challenge in such conversation is that, “often a lot more is told by what is not said in a sentence or conversation than what is said.”
Such case poses another major challenge such as; the narrator/communicator could deliberately skew the outcome. “For example, one may pretend to be angry, or one may also lie. Thus, there can be a many false positives involved. The primary task will be to categorize or estimate the emotions of the communicators and then compute the probability of whether the conversation as successful or not.
Thus, in these cases, the measure of quantity of data is important, but the quality of data is much more vital. The first step will be to make sense of what is communicated. To do this, a very clever algorithm will be required. The algorithm should be modern algorithm, so that, it can interpret the modern form of communicative languages used in social media. Next the task will be to detect the emotion of the persons involved in the conversation. If the form of communication in the social media is in audio format, transcript of such conversation needs to be made. Then the emotions involved should be classified under “normal or neutral?? and “agitated or excited??. Then, the probability of whether the conversation as successful or not is computed.
The aim of the Natural Language Processing (NLP) algorithm is to design and build software that can be able to analyse, comprehend, and create languages that can be used by the humans naturally, in such a manner that humans will be able to communicate with the computers as though they were communicating to other humans and vice versa.
Using Natural Language Processing algorithms to process Social media data and present it with a simple visualization platform involves use of a combination of knowledge-engineered and statistical/machine-learning techniques to disambiguate and return to natural human language input. It will involve implications for applications like text critiquing, data recovery, query replying, summarization, and translation. Further, the interpreted data has to be represented in visualization platform for the users