3 reasons why audio sentiment analysis is complex but AI can already make it useful

Audio sentiment analysis

What is audio sentiment analysis?

Sentiment analysis has become a buzzword in the world of technology, particularly in the field of natural language processing (NLP). It is the process of identifying and categorising the sentiment expressed in a piece of text or speech as positive, negative, or neutral. While it may seem straightforward to analyse sentiment in written text, the task becomes significantly more challenging when it comes to audio content.

Audio content is inherently more complex than written text because it involves not only the words spoken but also their tone, intonation, and other vocal cues. Then, there can even be non-verbal cues such as laugher, sighs or groans. It’s a complex tapestry and communication but humans are experts at picking up on these subtleties, but machines struggle to do so, and as a result, audio sentiment analysis of audio content is a significant challenge in NLP.

Why is audio sentiment analysis so hard?

Let’s consider the example of the brand Ford to understand the complexity of audio sentiment analysis in audio content. If the word Ford is mentioned in an audio clip, we could isolate the word itself and analyse how it is said, the intonation, and energy to draw conclusions on whether the mention of the brand Ford was positive, negative or neutral. However, it is essential to look at the immediately surrounding content to gain a more accurate understanding of the sentiment expressed.

For instance, if the sentence was “I own a Ford, and it never breaks down,” we could infer that the sentiment is positive because the speaker is expressing satisfaction with their car’s reliability. However, if the following sentence were “I hate my Ford because it is always breaking down,” we would need to adjust our sentiment analysis accordingly and say that this is negative. So far, so good.

Furthermore, the context in which a brand is mentioned can significantly impact the sentiment expressed. For example, consider the two scenarios mentioned earlier about a Ford car in Cambodia during the reign of former Cambodian dictator Pol Pot. In the first scenario, the speaker is expressing satisfaction with the car’s reliability, and hence the sentiment is positive.

In the second scenario, the Ford car is associated with unreliability and thus the sentiment is negative. Now if we say the Khmer Rouge only used Fords because of their reliability and it allowed the Cambodian genocide to be effective and ruthless, what is the sentiment now? The sentiment about Ford could still be positive, good car, reliable, but the overall sentiment is probably negative and one that the brand Ford would not want to be associated with. So is this brand safe and suitable?

How to create rules for audio sentiment analysis

The complexity of audio sentiment analysis in audio content is not just limited to the nuances of language and context. There are also challenges posed by the variability of human emotion and the different ways it can be expressed. A person’s tone of voice and intonation can change dramatically based on their emotional state, making it challenging to identify and categorise sentiment accurately.

Another challenge is the fact that different people may interpret the same piece of audio content in different ways. For example, a person’s cultural background, personal experiences, and beliefs can all impact how they perceive sentiment in audio content. As they say, “one man’s terrorist is another man’s freedom fighter”.

Given the complexity of audio sentiment analysis in audio content, it is not surprising that machine learning models struggle to perform this task accurately. These models rely on large datasets to train themselves, but the quality of the data can significantly impact their performance. If the training data is biased, the model may be inaccurate, and if the data is too limited, the model may not be able to capture the full range of sentiment expressed in audio content.

In conclusion, sentiment analysis in audio content is a complex and multi-layered task that requires a deep understanding of language, context, and human emotion. While machines are getting better at performing this task, they still have a long way to go to match the accuracy of humans. As the use of audio content continues to grow, it is crucial to develop better sentiment analysis techniques to extract meaningful insights from this rich and complex data source.

Here at Sonnant we work with customers to define sentiment so that the Sonnant platform can adequately identify and ring-fence problematic sentiment. This is the next wave of brand safety and suitability, making sure contextual targeting is advantageous for publishers and brands.

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