4 Applications of Natural Language Processing that you Should Consider for your Business in 2018.

applications of Natural language processing

80% of the data generated by today’s businesses is unstructured. Most of it is generated from conversations with customer service representatives and on social media platforms. A lot of data about the companies can be found on review platforms such as Yelp or TripAdvisor and Q&A platforms such as Quora, as well as other Internet platforms. Organizations are turning to Natural Language Processing (NLP) technology to derive understanding from the countless unstructured data available online and in call logs. Here is the list of most common applications of natural language processing technology that help businesses better engage with customers:

1. Information Extraction

Information extraction is the task of automatically extracting structured data from unstructured or semi-structured machine-readable texts. Full-text extraction of Internet content usually includes: entity extraction (names, places, sums of money, keywords), text categorization (by sentiment, by industry, by purpose or by other necessary categories), clustering (identifying main and new topics), fact extraction (allows to fill databases with structured information for analysis, visualization, and alerts), relationship extraction (is used to fill out graph databases to explore real-world relationships). Let’s see what it means in real cases.

Many of the business or product decisions are driven by information found in the news, on social media and numerous Internet platforms. Getting valuable and structured information from these sources is not easy. The majority of such content is present in the form of text, infographics, and images. The main application of natural language processing is in taking these texts, analyzing and extracting the related information in one of the formats that can be used in a decision-making process. For example, news of a merger between companies can have a big impact on trading decisions. The speed at which the merger, players, prices, can be incorporated into a trading algorithm can have profit implications in millions of dollars.

2. Sentiment Analysis

Sentiment analysis solutions are used to extract meaning from millions of messages. They identify sentiment among one or several sentences in the post in order to measure overall public opinion on a particular brand or event. The emotion in such posts might not be explicitly expressed, or it can be mixed, and sentiment analysis solutions have to cope with this issue. Sentiment analysis helps companies with the following tasks:

 

  • sentiment analysis as a competitive advantage

Knowing the general sentiment of your company’s audience doesn’t give you much, but modern sentiment analysis solutions allow to compare your indicators to those of your competitors’. With accurately adjusted text categorization, companies get valuable information about aspects of their business they need to improve, and what is it exactly that does not satisfy their customers.

 

  • sentiment analysis for better customer experience

Sentiment analysis provides companies with a much-needed assist to stay responsive to their customers. Companies have long depended on data from customer surveys, call center interactions and focus groups. Such data was structured, kept in one place and handled through BI applications in order to help in improving customer service and product enhancement. Such approach is very slow and doesn’t allow to act in a proactive manner.

The situation has radically changed with the emergence of social media and various online platforms, where the majority of companies’ customers, both satisfied and furious, are dwelling. And this is their opinion that counts and shapes brands’ perception and sentiment. Sentiment analysis solutions allow to quickly analyse large amounts of data and address the potential issues.

 

  • sentiment analysis for adequate brand perception

Brands are not defined by their products and services. Fame that builds a brand majorly depends on online marketing, social campaigns, and work of customer support services. Keeping past sentiments in mind brand managers can constantly improve their performance and develop more appealing branding techniques.

There are many other possible applications of natural language processing that remain to be seen. Have an idea but need some help implementing it? Contact us and we will help.

3. Semantic Search

The way people consume information has dramatically changed in last two decades. It wasn’t long ago when we turned to libraries, dictionaries, phone books, and paper newspapers for information. Now we simply turn to the web for answers. Answering more complex questions could take days, now we get the answers in mere seconds. The accuracy of such answers majorly depends on how “smart” is the search. This is why the best search engines are now powered by natural language processing technology. It allows to provide information requested by a user rather than making him go through all the related keyword results.

Semantic search engines can already be found in browsers, smartphones, e-commerce platforms, corporate programs such as CRMs, mobile applications, you name it.

The number of long-tail search requests on the Internet is increasing every year. This means that consumers are getting used to massively utilize semantic search engines, and the applications and platforms that still use old-fashioned keyword search engines will continue missing out on potential sales.       

4. Question answering

Modern search engines can supply us with lots of useful information, but when it comes to answering really specific questions asked by humans, the answers are still quite primitive. This is where chatbots are gaining trust and popularity. Analysts predict that they will take over certain customer-service functions in less than five years. Chatbots are able to provide automated, real-time responses to simple customer-service problems and questions.

Research around question answering solutions is based on dealing with a wide range of question types including fact, list, definition, how, why, hypothetical and other types.

QA solutions can be divided on closed-domain question answering and open-domain question ones. Closed-domain question answering deals with questions under a specific domain, and it can be seen as an easier task because natural language processing systems can exploit domain-specific knowledge frequently formalized in ontologies. Alternatively, closed-domain might refer to a situation where only a limited type of questions are accepted, for example, questions asking for descriptive rather than procedural information.  

Open-domain question answering deals with the questions about nearly anything, and can only rely on general ontologies and world knowledge. On the other hand, these systems usually have much more data available from which to extract answers.

Things to consider

Natural language processing technology implementation is associated with two major challenges. The first one is that that there is no universal domain-independent NLP solution. A solution that works for financial industry will not work for a gaming company. Text analysis solutions need to be trained and customized for each particular case.

The second challenge is that it is hard to predict how long training will take, how accurate the results will be, and how accurate they have to be to deliver business benefits.

Overcoming these challenges will help NLP service providers gain more and more trust among their clients, meanwhile using natural language processing for creating a seamless and interactive communication between humans and machines will continue to be a top priority for today’s cognitive applications and products.

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