Completed 12 Years
Last Updated: 25 Mar 2019 | 5 min read
Ever wondered what happens when you talk to your Google assistant or Siri for the nearest diner? Of course, you get a list of nice and fancy restaurants in your area. But what goes behind the process is a complex thread of high-end code is generated. This sequence lets your voice assistant understand your query and respond. In other words, machines are now able to comprehend our language which was the other way round previously. Initially, to interact with computers we adopted their language pattern which is understood by them.
This is accomplished by the use of the technology called Natural Language Processing (NLP). This has already picked up the pace to bring some futuristic change in the world of business.
Natural Language Processing is a branch of Artificial Intelligence facilitating computers and machines to understand, interpret and manipulate human language. Drawing multiple disciplines including computer science, computational linguistics and mathematical calculations to bridge the gap between human communication and computer understanding.
Starting from an early age, computing methods have been utilized to examine human language and since then the growth & development in the same field has led to the popularity of this technology. The enhanced computational power led us to handle a huge amount of data that contributed to building complex mathematical models inclusive of thorough learning and adaptation of neural networks.
This has further streamlined the multi-channel data in the form of mobile data, social data which is creating a need for solid technologies to emerge so as to evaluate and access the sentiments of the customer.
The Current Stature Of NLP
Businesses today should quickly change and adapt to the model according to the current market trends and current exposure of techniques to identify customer actions does not suffice in this cutting-edge era of the business world. There is a dire need to understand the user behaviour, preferences & mood and to allow the technology to further application of NLP more user-oriented and will also drive as a roadmap for the future of business.
NLP and business intelligence go hand-in-hand. NLP can change the way we communicate with complex systems including database and datasets. Since NLP can support to access the business intelligence data which proves to be a way to connect non-technical with data. NLP application can make it easier for a non-technical person to start analysing data on their own and not necessarily wait for IT support. Hence, in a business network, NLP grants all the business personnel to access the information and make informed decisions.
The Future Of NLP
NLP contributes to business intelligence in many ways, especially in text analysis. For one, the business data originates in the unstructured format which NLP presents in a proper pattern that helps to organise and analyze further information.
The basic use of NLP in business intelligence can be obtained by analysing customer sentiments to obtain specific information from a text, this is also regarded as opinion mining. Many big companies and brands have started using it to map the mood of the customer from social media.
NLP further adds to the benefits of business intelligence with increased data access and better quality of data to be productive and efficient in the decision-making process by simplifying complex information into a simple structured format. You can instantly have business meetings and reports data or can even find eligible candidates in no time. Integration of business intelligence will present you with data-driven analytics at any given point of time.
The Final Picture
NLP requires no interface while the processing is carried out in the cloud. With the ongoing advancement in technology, computers will get better in understanding and responding to the query not only in terms of showing only search results. Machines will be able to understand the semantic relations and complexity of queries, it will automatically sort, filter and organize accordingly to produce a relative and significant respond rather than just showing you only the data.
Natural Language Processing is still in its early days as it can only narrow down common sentiments such as joy, fear, anger, sadness. However, in order for computers to completely comprehend the human language and sentiment analysis so as to understand, correlate and respond accordingly, much more needed to be done in the field of NLP.
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