How can Natural Language Processing (NLP) be applied to Big Data testing and analytics? In this post, we explore how to implement NLP for Big Data and the benefits of such a process.
Rather than just a buzzword, and a ‘good-to-have’ technology, big data is now one of the most important strategy implementation requirements in enterprises and start-ups alike. Big data is data being gathered constantly from various channels like customer’s online shopping behaviour, social media activity, and internal data logging like point-of-sales data, etc. The real-time data being logged keeps pouring in at all times and is stored in the cloud. Large enterprises can have more than petabyte scale data.
As the data is just being taken from its origin in ‘as it is’ condition, it is largely unstructured in addition to being constantly growing. The format of this big data is mostly text, that is shared on social media or customer feedback that is received on the products. In other words, the big data is in natural language, i.e. the kind of string of words a normal human being may use in real-life conversations.
NLP For Big Data
This kind of data needs Natural Language Processing (NLP), which is a form of machine learning algorithm, to analyse its contents. NLP is seen as the next big thing in data analytics that provides the ability to harness big data to derive information using innovative methods to produce useful insights on market trends – current or projected.
Although research on NLP is being conducted since quite a few decades, the field has shown significant progress only in last 3 years. Machine learning methodologies that use NLP are now being deployed extensively across enterprises through their partner big data consulting company.
NLP studies the patterns emerging in the text entries in the big data by analysing the linguistics and semantics through statistics and machine learning and extracts the significant entities and relationships in the context of what the customers are trying to say in their posts. Essentially, instead of focusing on a word or a string of words, NLP comprehensively analyses sentences for their intent. The most common methodologies that are used in NLP are automatic summarization, disambiguation, part-of-speech tagging, relations extraction, and entity extraction, and most importantly – natural language understanding and recognition.
How can NLP help leverage information contained in unstructured big data better?
In every domain – say medical, legal, pharmaceutical, sports, education, and so on, large chunks of data are archived daily in the form of documents, customer inputs, sales information, etc. This data is largely text, and hence NLP becomes vital to get effective results out of the analysis – be it predictive, real-time, or historical.
NLP can help in the following areas:
Siri in iOS is an excellent example of NLP in the interactive field. Online banking and retail retail self-service tools also make use of NLP, as do automatic translation applications. The expertise of interaction handling using NLP has evolved to levels where traditional customer care calls can now be effectively handled and resolved by the artificial intelligence implementation.
- Business Intelligence
Tracking a certain social media tag may require the analyst to input all possible ‘hashtags’ and keywords that cover the topic. NLP can perform search operations on queries entered in natural language, thus covering all possible scenarios and minimise the statistical errors in determining how many people are talking about the topic.
- Sentiment analysis
Brands can now gather information other than the official customer feedback through direct channels, from social media chatter. NLP can draw a conclusive picture of whether a particular product or service is being welcomed in the targeted market segments – demographic and/or geographical.
It is estimated that by 2020, all the big data analysis by each Big data Solution provides will be performed using NLP, as data size will exceed 44 trillion gigabytes worldwide so the scope of NLP for Big Data Analytics will only grow.