Sentiment analysis in customer service

Today, the personal customer experience plays an increasingly important role throughout the entire buying process. This means that you risk losing customers if this cannot be brought into line with their expectations.

With the use of artificial intelligence (AI), customer satisfaction can be improved. The Sentiment Analysis method can detect the sentiment (positive, negative or neutral) of a customer review/message. This allows dissatisfied customers or problem areas to be identified and the company can respond to them proactively.

What is Sentiment Analysis?

Sentiment Analysis can be used to identify the mood in a text. On the one hand, the polarity of the text presentation, i.e. whether the text is positive, negative or neutral. Secondly, the feelings or emotions in the text, i.e. whether the text contains happy, angry or sad elements. In addition, the urgency or interest of a concern in the text is also recognised.

For successful sentiment analysis to be implemented, the system must overcome several challenges. Sentiment analysis is context, culture and language specific. This means that words have different meanings in different environments and domains. For example, a statement can have both a negative and a positive meaning and can only be clearly identified in context. Also, some texts contain positive as well as negative mood elements. Furthermore, depending on the culture, expressions are also to be evaluated differently. It is also challenging to recognise sarcasm algorithmically. These challenges can all be solved with the help of machine learning (ML).

There are two ways to implement a sentiment analysis system. One is through a simple rule-based system. This calculates the number of negative and positive words and uses them to classify the texts as either negative or positive.

On the other hand, an ML model can be used for the classification. These models must first be trained with existing data, but can then automatically estimate the sentiment of new texts.

The development of such an ML model is more time-consuming than rule-based algorithms, but it can also deliver more accurate results.

There is generally no blanket solution for such a system. Individual challenges have to be addressed with use-case-specific solutions.

For more on the topic of sentiment analysis in customer service and brand monitoring, as well as an example of how a sentiment analysis system can be developed, read here our article "Sentiment Analysis in Customer Service" in the eBook "Artificial Intelligence in Customer Service" by Sigs Datacom. For an English version of our article, please contact us.

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