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Sentiment Analysis: How AI is Capturing the Voice of the Customer

Expert.ai Team - 13 May 2021

Five painted eggs expressing different emotions.

All too often, brands focus on the hard data when it comes to customer feedback. As long as their rating in the app store or on Google sits above four stars, all is well. But does that number really tell you anything about your brand or products? No…at least nothing of real value.

The voice of the customer cannot be translated into a number or another measure of structured data. The voice of the customer must be read, processed and, most importantly, understood. With countless channels upon which customers voice their support, disdain and litany of other feelings, brands must be there to hear them. More importantly, they must use that sentiment to their benefit.

Monitoring and analyzing the sentiment of customer opinions and feedback should be every brand’s priority, but brands have long struggled to process this data (especially at scale) and turn it into actionable insight. With artificial intelligence, that no longer has to be the case.


What the “Voice of the Customer” Can Unlock for You

Using sentiment analysis, companies can detect the opinions expressed by users and measure customer feedback found in millions of web pages, postings on review sites and social media platforms. In layman’s terms, it intercepts and analyzes each kind of text and understands if the sentiment is positive, negative or neutral.

For organizations that want to know what users are saying and how they feel, sentiment analysis provides strategic value from the extraction of online feedback and comments. It also serves many other purposes including:

  • optimizing the impact of content and brand messaging
  • defining customer engagement strategies
  • identifying trending topics and influencers
  • supporting proactive engagement to foster brand awareness and improve customer service
  • revealing specific insights on market and competitors


Artificial Intelligence (AI) Approaches to Sentiment Analysis

Companies have leveraged sentiment analysis for many years, but they have experienced varying degrees of success. The root cause of this issue lies in the artificial intelligence approach used to process the text-based data.

Typically, organizations are presented with two alternative options:

  • A rule-based (or symbolic) approach has humans label words as either positive (+1) or negative (-1) so that excepts can be given a sentiment score based on the net polarity score of the text.
  • A machine learning-based approach trains the model on documents pre-labeled as positive, negative or neutral. After sufficient training, the model can then infer the sentiment of new pieces of text.

Both options can enable sentiment analysis on a broad scale for your organization. However, they do not equally address the main challenge associated with sentiment analysis: context.

Context is everything when it comes to sentiment analysis. The same words can carry an entirely different tone simply based on the question asked. For example, you could send a survey to customers asking both “What do you like about the product?” and “What do you dislike about the product?” The response of “Everything about it!” would express completely different sentiment depending on which question it answers.

Sarcasm and irony are both issues of context as well. While sarcasm is difficult to pick up in general, especially in text, it can be diagnosed more easily with the right contextual clues.

Knowledge is the key to deciphering context, but it is only accessible through a robust knowledge graph. Rule-based approaches that leverage a knowledge graph are thus much better equipped to provide accurate and actionable sentiment analysis than their ML-based counterparts.


Sentiment Analysis in a Hybrid AI Approach

Expert.ai’s natural language technology understands the nuances of language, which is especially important when it comes to social language (including the use of slang, acronyms, tone, abbreviations, etc.) and the unique ways that customers express themselves on social media.

Its comprehension is essentially equivalent to more traditional focus groups and surveys but without the time and expense. It also provides regular, timely updates that enable companies to capture real-time awareness of customer opinions, trends and events being discussed online.

The voice of your customer is ultimately the voice of your brand. Make sure it’s heard and, most importantly, make sure it’s understood.

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Sentiment Analysis: How AI is Capturing the Voice of the Customer