Natural Language Processing: It’s not a big deal if you are hearing this term for the first time. Natural Language processing is a machine learning paradigm that derives meaning from human language.
How often do we think about the intricacies of the languages we speak? Our languages are comprised of complex grammar and syntax. Moreover, these languages do not have consistent rules. They are filled with exceptions. Many languages including English have different verbal pronunciations and written spellings. These issues derive the need for Natural Language Processing.
Natural Language Processing finds its use in many sectors. It has numerous applications in converting user input into machine level language. It might appear as a super-complex, innovative, and cutting-edge technology. But it has become a normal part of our daily lives now. Users all over the globe interact with it, without even realizing it.
Let’s learn more about the applications of Natural Language Processing.
- Social Media Monitoring – Natural Language Processing
This tops the list of applications of Natural Language Processing. There are numerous reasons for this.
Social Media Monitoring tools like Hootsuite or Buffer are based on NLP technology. You must have encountered such tools if you have been using Social Media Monitoring tools.
Tools like these help you to monitor social media channels for mentions of your brand. They alert and notify you when customers are talking about your brand.
Many marketers and business owners will know that a brand’s reputation can be destroyed overnight if a negative review goes viral. Keeping this in mind, brands need to engage in social media monitoring or listening to ensure that they respond to any potential crisis immediately.
- Sentiment Analysis
Sentiment analysis is the next item on our list. This is also a subset of social media monitoring.
The latter works to monitor the social media landscape and listen in on conversations as a whole. On the other hand, the former deals specifically with identifying opinions and determining whether a consumer holds a positive, negative, or neutral opinion towards the brand.
NLP plays a crucial role here. The algorithm behind it is to pick out emotionally-charged. These are the words used to describe a brand or a customer’s experience with a brand.
As an example, consider a pos that contains plenty of positive language such as “amazing”, “fantastic”, “wonderful’, then the tool concludes that the overall statement is positive.
With the help of sentiment analysis, brands can gauge how responsive their consumers are towards a product. It also tracks their response to their recent changes. For instance, a change in the return policy, support policy, customer service, etc.
- Text Analysis – Natural Language Processing
Text analysis comes next in our list. It can be broken into several sub-categories. These include morphological, grammatical, syntactic, and semantic analysis.
Companies can organize their data better by analyzing text and extracting different key elements. These elements are keywords, topics, people, dates, locations, companies. From there, they identify useful patterns and insights.
As an example, the eCommerce websites conduct text analysis of their customer reviews to know what they like or dislike about their products. To accomplish this, companies use NLP based tools.
- Survey Analytics
The companies also need to analyze their survey results apart from their reviews to come up with actionable insights. NLP plays a crucial role here as well. It helps these companies to extract meaning from all their raw data. After this, it generates useful insights and takeaways.
Companies that are conducting small-scale surveys might analyze them on their own. But if you are surveying an entire database of 10,000 customers, then it’s not possible to sit down and go through the results yourself. Hence, automating the process with the help of an NLP based tool is a wiser idea.
Google predicts what you are looking for or searching for with the help of Autocomplete. It does this by considering the few initial characters or words of your query.
Google relies on a treasure trove of data to suggest those relevant keywords for you. This data includes what other customers are looking to find when entering such specific search terms. The companies use NLP to make sense of such data and understand the subtleties between different search terms.
If you have fat thumbs or text people on the go, it’s hard for you to hit the right keys on your keyboard. This is where you appreciate the beauty of autocorrect. Autocorrect also relies on NLP technology like autocomplete. Here, NLP follows an algorithm that identifies the closest term to your misspelling. It then automatically changes your misspelled word to the accurate one instead.
- Smart Search
When you search for something on a search engine or website, the website returns relevant results. Thus, you find what you were looking for. This looks so simple but has a very complicated framework behind it.
NLP again comes to save the day here. With NLP, the engine can pick up on context and contextually relevant synonyms to search results. This allows the store to predict more accurately what exactly their customers are searching for, and highlight relevant listings.
- Translation Tools
If you visit someplace where English or your native language is not used and understood, you will definitely need a translation tool. The most popular translator tool so far is Google Translate. It is used by more than 500 million people every day. It understands more than 100 world languages. It uses NLP to analyze and understand the phrases or terms its users are trying to translate. All the other translation apps follow the same algorithm.
Want to avoid the tedious waiting period to connect to a customer service agent? Chatbots serve this end of customer service with sheer smoothness. Did we ever wonder how are chatbots able to understand what we type?
The credit goes to NLP here as well. With the help of NLP and machine learning, chatbots can understand user’s questions. It can then recommend products, book meetings, tickets, or appointments for them, and much more stuff.
- Smart Home Devices
Summing up our list of NLP applications, we have Smart Home Devices. The most popular ones are Google Home and Alexa. They are becoming very popular nowadays, especially among young consumers. They use NLP to analyze the voice commands of the user. They are great for multitasking and are the technologies of the future.