Applications of natural language processing, usecases of nlp, chatbots, sentiment analysis, spell checking, virtual assistants
APPLICATIONS OF NATURAL LANGUAGE PROCESSING
Chatbot’s
A chatbot is one of the important applications of Natural Language Processing. It is used in many
companies to provide customer chat services which are in many other services
while you're interacting with. Let's say amazon has chatbot services where
you can inquire about your product or when it is shipping or is that shipped
or not or why there is a delay or if you want a refund of something so that
can be done.
Contacting support through a chat window. It can respond in real-time and
understand what input providing you the pertinent information immediately.
Language processing is often the technology that's instantly responding in
that chat window. It's often doing this without human intervention.
Search Auto-complete and auto-correct
When we search on google it shows the possible search terms. Ever searched
and it filled in what you're looking forever have to auto-correct fix a word
can you imagine writing a paper without spell check?
Machine translation
It is a process that translates text from one natural language to another
natural language. Google translator develops with the help of Natural Language Processing.
Hiring and Recruitment
Human resources often leverage natural language processing technologies to
analyze volumes of resumes. This helps them identify, diverse, and qualified
applicants. Natural language processing allows for less bias in the
recruiting process while expanding the number of potential candidates.
Social media monitoring
Another application of this technology is in self-awareness and monitoring
your emotional state. It can help identify depression and anxiety by
analyzing interactions you have on social media platforms text or even in
conversation.
Advertising
Natural Language Processing is used for intelligent targeting and placement of advertisements for
the right audience at the right time. NLP is also used for quality digital marketing.
Sentiment analysis/ Opinion mining
Sentiment analysis is used on the web to analyze the attitude-behavior and
emotional state of the sender. Sentiment analysis is selecting the text and
define the feeling or emotions. This application is implemented with
the combination of NLP and with certain values of text. Let’s see the
positive or negative or neutral. So these all values have been given or
there is the mood of context and give a rating to several things or giving
your experience. So NLP is behind all this, nobody is sitting to analyze
every user's reaction. So what opinion or whatever feedback the user has to
give that is done the whole data process along with the help of NLP. But you
might have seen several emojis also for depicting that whether you like the
product or it's okay or extremely happy with the product or not.
Natural language processing also could be used for social good and
well-being. It could identify sentiment in conversations and written
communication. This technology could flag at-risk youth and immediately
recommend help or intervention.
Question and answering
It is a task that focuses on building a system that can automatically
answer questions by a human. Google assistant- Alexa, Siri they all answer
in human language only. So there is an application of NLP in question
answering. It helps the user to ask a question about any subject and get a
direct response within a second. It is time-efficient also and it helps the
computer to communicate with a human in their respective languages. That is
the major advantage of examples is Amazon Alexa, a robot that can ask
certain questions and will reply in your language.
Spam detection
Spam detection is used to detect the unwanted emails that are getting into
the user inboxes. Suppose you get an email now using the machine learning
model and it classifies that whether it's spam mail or not.
Spell/ Grammar Checking
Processes like MS word, PowerPoint all do spelling correction or we can say
if that is correct and that is not recognized then we ignore all simply do
that. So all those corrections are done with the help of NLP because it
recognizes what text you have written and it identifies the particular text. But the user has entered it in the wrong way. It changes it or modifies the
particular text. Also, there are several tools such as Grammarly which also
writes the correct text in place of the wrong text which you have written.
Speech recognition
It is used to converting spoken words into text. It is used in applications
such as mobile, home automation, video recovery.
Text mining and analysis
It extracts meaningful data from the natural human language text.
Virtual Assistants
Siri, Cortana, Alexa, Google assistant- all use patterns in speech and text
to respond, recommend, and take action. They can instantly respond to
context and semantics to provide relevant information to you. Virtual
assistants may very well become the biggest and most used consumer-based
platform.
Market Analyses are used in the financial sector.
There are many products in the world developed using NLP for entertainment
purposes.
Security has been upgraded to even further using NLP-based applications
such as data visualization and biometrics.
Customer feedback analysis and service automation.
Geographic analysis and COVID- 19 Impact Analysis in Healthcare and Life
Sciences with the help of NLP.
For example improving the user experience information extraction, document
summarization, search engines, advertisement matching, classifiers, spam
classifiers, news feeds. All these applications are built on using natural
language processing. These real-world text-based applications are all
founded upon natural language processing techniques.
Future of natural language processing
In the upcoming years, there will be significant advancements in this
technology. It is rapidly becoming so powerful and manipulating language. It
is virtually indistinguishable whether a machine or a person was the
creator. Natural language processing will likely be one of the leading
technologies to impact our lives.
Some of the open-source NLP libraries
·
Natural Language Toolkit (NLTK) - It is an open-source python library for
text processing.
·
Apache OpenNLP
·
MALLET
·
Stanford NLP
·
Scikit-learn, Keras, Tensorflow – text processing capabilities.
COMMENTS