Know how our voice bots and chatbots can be the most efficient AI to understand your customers’ goal.
Sep 07 • 3 min read
Natural language processing is one of the most promising fields within Artificial Intelligence, and it’s already present in many applications we use on a daily basis, from chatbots to search engines. We at GenieTalk use the NLP methods along with Sentiment Analysis and NER, which makes our voice and chatbots the most efficient AI to understand your customers’ goal.
Let’s dig a little deeper!
Natural Language Processing
“NLP is the practice of understanding how people organise their thinking, feeling, language and behaviour to produce the results they do. NLP provides people with a methodology to model outstanding performances achieved by geniuses and leaders in their field. NLP is also used for personal development and for success in business”
Natural Language Processing (NLP) is a part of Artificial Intelligence (AI) that reviews how machines comprehend human language. It will probably assemble frameworks that can comprehend the message and perform tasks like translation, interpretation, or grammar checking. NLP makes it possible for computers to understand human language. Google assist, Siri, Alexa, chatbots are the most popular examples of NLP.
Sentiment analysis is the process of breaking down the content (documents, News articles, social media conversations, etc.) to assess the polarity of opinions (positive to negative sentiment) or tone, goal and feeling. With the proper software, sentiment analysis can even read for things like sarcasm.
We at GenieTalk follow a similar set of processes to understand your customers' needs and help them to achieve their goal.
How do we do that?
Our bot does more than just analysing the text, it also focuses on feelings and emotions, and even on intentions (e.g. interested v/s not interested).
Here are some of the most popular types of sentiment analysis:
Polarity sentiment analysis:
This is the most common type of sentiment analysis, which classifies the emotional tone of an expression as Positive, Negative or Neutral.
Fine-grained sentiment analysis:
It is the classic 5-star ratings you often see in reviews. The 5 categories are Very Positive, Positive, Neutral, Negative, Very Negative.
This focuses on identifying specific emotions, like happiness, anger, frustration, sadness, etc.
Aspect-based sentiment analysis:
Besides classifying opinions based on polarity, aspect-based analysis enables you to attach emotions to specific topics.
Named Entity Recognition
Named entity recognition (NER) ‒ also called entity identification or entity extraction ‒ is a data extraction method that automatically identifies named entities in a text and classifies them into predefined categories. Entity extraction is really useful for analyzing unstructured text.
NER is a form of natural language processing (NLP), a subfield of artificial intelligence.
With named entity recognition, we obtain key information to understand what a text is about, making it a great starting point for our bot to understand your customers more efficiently.
Any NER model follows the two-step process:
* Detect a named entity
* Categorize the entity
Organizations are progressively utilizing NLP-prepared tools to pick up bits of knowledge from data and to automate routine assignments. The Sentiment Analysis, for example, can assist brands with recognizing feelings in text, such as negative comments on social media. Named entity recognition (NER) is also an essential NLP task that allows us to spot the main entities in a text.
Talk to us to create a customised voice/chatbot for your organisation.