You see them on more and more websites and social networks. In the form of animated avatars or simple dialog boxes, chatbots are everywhere on the internet. They are able to answer questions, help you solve a technical problem, order a product or direct you to the service that best suits your needs.
It is also on this technology that virtual assistants like Siri, Google Assistant, and Microsoft Cortana are based. For many experts, chatbots are even destined to completely replace mobile applications. on Big Data Training
The increases in computing power, the progress made in the field of artificial intelligence, language analysis, and machine learning, are among the innovations that have allowed chatbots to become more efficient. Be that as it may, these robots committed to characteristic correspondence are additionally firmly connected to Big Data. Find the connections between chatbots and big data.
How Conversational Agents Collect Big Data
The primary role of a chatbot is to respond to a client's request. However, interactions with users are also sources of data that the chatbot collects and stores in a database.
Review that big data or huge information are characterized by three attributes, the 3V: volume, speed, and a variety. However, the data generated by conversations between chatbot and internet user fulfill these three conditions.
How Conversational Agents Analyze Big Data
The operation of chatbots is based on data analysis. Thus, when a surfer sends him a message, the chatbot does not really understand what he says or asks him in the same way as a human.
To understand a query, the chatbot uses pattern recognition. He will recognize certain terms or groups of words in order to deliver a response associated with these terms. On Big Data Certification
The most advanced conversational agents are also able to analyze a user's way of speaking and associate a feeling with it. This is the technique of "feeling analysis”. This allows companies, for example, to quickly detect a customer's frustration in order to solve problems first and improve customer support
How Companies Harness Chatbot Data
Until recently, the primary sources of business-usable customer data for personalized marketing were e-mails or social interactions. Today, however, data collected and analyzed by chatbots is of great value to businesses.
With natural language processing and demographic analysis, this data can, for example, be used to detect trends and develop personalized messages using the same language as the target.
It is also possible to analyze the frequency of certain problems reported to customer service to determine which products and services are most problematic. Adequate measures can be taken.
In addition, this data can also be used to create a recommendation engine. This is to combine the data collected by the chatbot with the predictive analysis technique to offer each customer products and services that meet their needs. In general, chatbots can improve the customer experience. Get More Points on Big Data Online Training
Chatbots can also be used to communicate with customers via mobile applications, including email applications. For example, at its 2016 F8 developer conference, Facebook launched chat bots support for its Messenger platform. Other messaging applications like Telegram and Slack are also compatible with chatbots.
how chatbots improve with the data they collect
The chatbots themselves can be improved with the data they collect. This data can be used to re-feed the deep learning algorithms on which chatbots are based in order to increase their intelligence.
As you can see, Big Data is at the heart of chatbots and chatbots themselves generate valuable data. Over the next few years, conversational agents will continue to improve until it is impossible to differentiate them from a human interlocutor. They can, therefore, be expected to extend to more areas of application.
How to create a chatbot with Big Data?
In fact, the very operation of chatbots is based on Big Data. Indeed, a chatbot is created is driven from datasets.
The data necessary for the operation can come from different sources. These can include queries to customer service, information stored on the company FAQ page, call logs, or open data sets made available by governments or others. Organizations.
The data is then converted into a structured form from which chatbots can take. It is possible to develop the chatbot using a decision tree, but it will be much simpler and more efficient to use natural language processing (NLP), natural language comprehension (NLU) and Machine Learning. More Information on Big Data Hadoop Training
It is then sufficient to provide data sets to the robots to enable them to train before being deployed in real life. For example, NLP can use a declarative approach to allow chatbots to recognize intentions and entities. Large amounts of example sentences will then be used to tell the robot which terms are important in a conversation and what the user wants.
Subsequently, once the chatbot is deployed, it will be possible to analyze the conversions to detect the sentences that it has trouble analyzing. We can then opt for a "supervised" machine learning approach, consisting of manually adding new example sentences to help the chatbot improve its main weak points On Big Data Training In Bangalore