Learn all about how sentiment analysis can help increase engagement with your customers to better help them find the information they need.
Conducting sentiment analysis real-time with your chatbot user interface is a challenging proposition that can yield successful results if you are able to understand your customer’s needs and be able to pivot the conversation. While the technique remains an emerging technology, most of this innovation continues to evolve as chatbots become more mainstream, especially with enterprises now funding resources to prove out the technology. Sentiment analysis has become more popular due to the large advances in deep learning, which has brought about cutting-edge algorithms to better extract the intent of a human’s text and voice communication, thereby scaling it at large.
All About Chatbot Sentiment Analysis
Sentiment analysis sits above a chatbot’s capability for natural language processing and understanding engine (NLP/NLU). It allows the bot to comprehend and understand the sentiment of the user by breaking down the sentence structure as well as verbal clues within a user’s response. The ability to understand the mood of a user during each interaction with the chatbot allows it to deliver the best user experience taking into account this additional layer of understanding the user.
Why Sentiment Analysis is Important
Sentiment analysis is important to businesses because it allows organizations to understand what people are saying about the brand, what they are saying, how they are saying it, and what they mean when the say it. In the age of social media, customer sentiment can be analyzed through the various channels that brands are marketing their products, from tweets, blog comments, online reviews, and online forums. Chatbot sentiment analysis provides an additional layer of better understanding users and their motivations.
In the modern world of online marketing, business leaders as well as developers can use the progress of natural language processing, statistical models, and text analysis to better understand the emotions of customers at the very least to understand whether they are neutral, positive, or negative categories.
How Sentiment Analysis Works
Sentiment analysis uses natural language processing (NLP) to group text from users and classify them at the very least as positive, negative, or neutral. This includes algorithms that include pre-labeled words that have already been categorized into these buckets. When a user says or types an utterance, the computer system can take that string, analyze it against the model with the algorithm, and then returns a scoring or rating for the confidence level for understanding which sentiment category the string falls under.
Challenges of Sentiment Analysis
While chatbots can prove to be an easy technology to standup with the plethora of SaaS chatbot providers, there are also challenges in this space that your organization may want to be aware of. The complexity of chatbot sentiment analysis is due to the different meanings that can be expressed in a single sentence. A chatbot’s ability to personalize and cater to user needs can be strongly enhanced with sentiment analysis, thus adjusting the conversation for a better outcome with the user.
Sentiment Analysis is an Emerging Technology
Sentiment analysis is an emerging technology that continues to be quickly refined. If your organization invests in this space, you may be leading the effort in research and development for chatbots. Therefore, as chatbot platforms continue to mature, your team may be left with technical debt if you have invested in customizing any models for sentiment analysis.
Before labeling conversations and utterances, the first challenge for chatbot sentiment analysis will be defining the buckets/categories of sentiments. How many different categories or buckets will your organization use? Will you include just positive, negative, and neutral? Do you want to include angry, bored, and sad? These categories can be different based on the chatbot’s use case.
Hiring Data Scientists for Personalization
Pre-trained models are available on most cloud platform providers, making it quick and easy for your team to get started with sentiment analysis. However, as you gather information and proprietary data within your organizations, you may find it difficult to build models that work for your organizations specific use cases. Hiring a data analysis or data scientists may increase the cost of maintaining your chatbot.
Implementing and Developing Chatbot Sentiment Analysis
When developing your initial chatbot, it does not necessarily need to include deep levels of sentiment analysis in order to produce a working prototype. Chatbot development lifecycles follow the same process as any software development lifecycle, and the same can be applied to adding a feature within the chatbot to better understand customers using services or models that can interpret sentiment analysis.
The full cycle of sentiment analysis implementation includes capturing human utterances and inputs, reviewing and analyzing the sentiments, designing and validating the sentiment scoring, creating a model and training it, analyzing the improvements, and starting the loop all over again.
There are several cloud providers that provide sentiment analysis as part of their AI/ML/Cognitive Services offerings, so you won’t have to hire a bunch of data analyst and data scientists off the bat. As your chatbot platform continues to grow with various skills and capabilities, the need to customize the models past what these platforms can provide outside of the box will become important as you continue to iterate on the customer experience with your chatbot.
Sentiment Analysis in Real-Time
Sentiment analysis should allow chatbots to adapt to users in real time in order to deliver personalized experiences using languages and tone based on the context of the user’s current mood. A chatbot’s followup question with a user who is angry should not have the same standard, robotic experience as a new user that is interacting with the chatbot.
Ways Businesses Can Leverage Chatbot Sentiment Analysis
Often, the question that confounds most business owners is how chatbot sentiment analysis can be used. It’s a great question because it can become a large maintenance investment as the use cases continue to grow with a chatbot platform. Determining whether an ROI can be had with a chatbot is one thing, but having data insights on your current and potential customers as well as attributing that to profit is another discussion.
Below are some of the examples on how chatbot sentiment analytics can be used for business.
Monitoring Brand Reputation
Key insights to how a business is doing along with specific examples can be measured by using sentiment analysis. The ability to understand an advertising/marketing campaign, measure impacts of new releases of services or product, and responding to company news can be used to provide an overview of how your company, brand, product, or service is viewed.
Customer Service with Human Transfer
Sentiment analysis can help pinpoint when a user is angry or frustrated and being able to automatically transfer that customer to a human. Furthermore at a deeper level, your human capital resources have now been removed from the first line of customer service inquiries to dealing with more complex conversations where customers are angry and are about to give up.
Conducting Market Research and Analysis on Current and New Products
Being able to understand customers as it relates to the value your products or services provides will allow you to understand whether or not they meet their expectations as well as get ideas for how to improve it. As market research becomes more programmatic through advances in AI and ML, companies can make better future predictions of how services and products will do when they are released using sentiment analysis through with research.
Categorizing Customers Using Conversation Mining
Chatbots can provide a full script of a customer interaction, data that can be used to create useful information to better categorize and identify customers and bucket them into categories based on the type of interaction they need.
The Data and Insights Behind Sentiment Analytics
There are various ways to review insights gathered from sentiment scoring. Most often, sentiment analytics deals with being able to take unstructured data and categorizing them at scale for interpretation (and being able to act on them). It can help paint a better picture of what customers’ needs are and have a tremendous impact on customer retention through personalization and empathy.
Organizing Unstructured Content into Sentiment Buckets
New data in the form of conversational chats is though to analyze and sort through. Sentiment analysis is one of many ways that businesses can help sort and label this data to parse information at a high level, thus continuing to develop the chatbot’s emotional intelligence and empathy towards the user.
Further Categorizing Sentiment by Topic Area
Sentiment analytics could be a powerful tool for any business owner to know the strengths and weaknesses of their business. Being able to create sentiment scoring by a business’s topics or area of expertise will provide a KPI that can be used to help improve the business in the future.
Feedback Models with Automated Scoring of Customer Inquiries
Implementing automated scoring after each customer service support engagement can provide real-time feedback to organizations, thus being able to create a feedback mechanism to utilize the knowledge trained and training future teams on how to handle similar customer engagement in the future.
Frequently Asked Questions About Chatbot Sentiment Analysis
What is sentiment analysis?
Sentiment analysis is the process in determining whether dialogue has a neutral, positive, or negative tone. It allows data analysts and conversational designers to do market research on users and better understand user experiences.
How is chatbot sentiment analysis used?
The choices customers make and the interactions they complete influence how customer choices, and chatbot sentiment analysis can provide clues and data to help respond based on a customer’s emotional state to steer the conversation into a positive outcome.
What is chatbot sentiment analysis algorithm?
Algorithms for sentiment analysis can be different based on a chatbot’s specific use case. Minimally, the algorithm should include an analysis on whether the input from the user is neutral, positive, or negative and being able to understand and score the interaction.
Is sentiment analysis easy?
Sentiment analysis has been made easier through cloud providers that offer it as a service, thus providing developers and chatbot designers a head start in creating chatbots with sentiment analysis. As your chatbot continues to mature its language understanding, analysis may get harder due to the complexity of all of the data it continues to try and programmatically understand.
How do you write a chatbot conversation with sentiment analysis?
Chatbot conversations with sentiment analysis will be able to create different pathways based on the categories of sentiments it is able to understand. Each interaction and resulting response can have dialogue that is created for a neutral, positive, and negative sentiment. Chatbots that include sentiment analysis conversations can get quite complex due to the various pathways that a chatbot would need to handle. However, each of these pathways represents a personalized engagement for the user, which can result in longer-term user retention and positive outcomes.
Summary of Chatbot Sentiment Analysis
A chatbot should look and feel as human as possible, and the best way to do that is mimicking empathy, which is the ability to understand the feeling of the human you are talking to and adapting the conversation based on those feelings. Sentiment analysis provides chatbot owners the ability to offer personalization to your end user at scale, while also improving the engagement rates for your customers with your business.
Chatbot sentiment analysis doesn’t have to be the first thing you implement with your chatbot, but it should be at the very least considered as a backlog item for your bot development improvement plan. Chatbots have led the way for real-time communications with customers to better enhance business-to-consumer interactions.
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