The industrial revolution replaced workers with machines, forcing more of them towards the services sector. The digital revolution is now attacking this area through chatbots that aim to be at least as good as entry-level customer service representatives or shopping assistants. Gartner says that by 2020, the customer will manage 85% of their interactions with a company without dealing with a human.

The goal is to create conversational instances which don’t sound or behave like robots, but as human-like as possible. To achieve this ambitious target, the chatbot needs to understand language, context, tone and even subtle nuances like sarcasm. The tool which can enhance this is sentiment analysis, a process which automatically extracts both the topic and the feeling from the sentence or voice input.

Natural Language Processing

Building a great chatbot is similar to teaching someone a foreign language: they need to learn vocabulary, understand syntax, semantics, and be able to construct answers. Each country and language is different. Therefore, best practices for a chatbot in English will not always work in another language and need to be adapted. However, there are some aspects which are similar. These include tokenization, namely splitting the input into parts small enough to be classified as syntax parts. At this stage, it is essential to identify the difference between singular and plural forms. Stop words like articles need to be removed, while prepositions and conjunctions need to be analyzed to get the meaning.

A fundamental class of words is negations. These words can change the outcome of the analysis entirely and need to be handled with care.

Once individual words have been identified and selected into classes, the text analysis software focuses the on the relationship between these by introducing semantics.

The great news here is that you don’t have to do all the steps yourself, there are some platforms that already have these mechanisms included.

However, just understanding what clients say is not enough to provide stellar customer service, you also need to learn about the feelings they experience during the interaction with your chatbot.

Sentiment Analysis

The reasoning behind using sentiment analysis is human psychology. When they feel happy or neutral, people tend to take bad news or frustration in a more accepting way. On the other hand, a client who is already sad, disappointed or mad about a product will have very limited patience. By understanding context right from the beginning, a chatbot can select the best course of action and apply very different patterns, even if the underlying problem is the same.

This kind of insight about the customer gives companies the opportunity to provide better service to their clients. It is a way to bring human-level services without having a team of call center agents available 24/7.

In fact, sentiment analysis is most useful when it detects those cases which should be transferred to a human agent. This approach prevents unwanted situations when a client who is already nervous is further enraged by the interaction with an impersonal system which can’t solve the problem.

Of course, an algorithm would have some trouble when a double negation appears. By looking only at the words, it would classify it as a negative sentence, while the actual meaning would be a positive one since one negative word cancels the other due to sentence logic.

Most chatbots have trouble understanding personal notes and context. Since algorithms rule these, they assume that the same words in the same structures mean the same thing to all people. In the case of voice interaction, an additional layer of tone can be analyzed, and frustration can be identified. In text interactions, this is impossible and could lead to misunderstandings.

Potential Problems

The most significant issue with generative bots who learn from their interaction with humans rather than following a predetermined script was highlighted by the experiment with Microsoft’s Tay chatbot. In less than one day, this chatbot became racist, homophobic, and a Nazi, just by interacting with users. Most likely this was just a way of having fun for users, but if the same happened to a customer service bot, it would compromise the brand for years.

Even when sentiment analysis is employed, chatbots still have limited abilities to answer all possible requirements from clients. Some of these are related to more complicated syntax, others to customers using sarcasm or ambiguous wording. When the chatbot is unable to provide a satisfying answer, it should at least admit defeat and pass on the case to a human operator.

Another problem is when the chatbot’s sentiment analysis algorithm is not sensitive enough to frustration and does not alert the operator promptly. It should look out for signs of failure and take immediate action; any delay could result in a damaged perception of the brand.

Sentiment analysis can fail since it aims to classify words as algorithmically describing certain feelings. Each person can choose their own way to express the same concept or idea, depending on their personality and cultural upbringing. These differences can be impossible to trace for a chatbot, and there are no simple solutions.

What Next?

As syntax analysis progresses and AI systems like e-bot7 GmbH become better by learning, we can hope that chatbots grow to be more human-like and will be able to detect our feelings. It will transform them into useful marketing tools and trusted replacements for call center agents.

The good news for call center agents is that chatbots will only take on the bulk of the work, yet they will never put people out of work. In fact, a way to evaluate progress is to track how fast a chatbot is able to identify a case that needs to be forwarded to a human.

This article was written by Sophia Brooke. Please check her other articles!