As the number of consumers increases and users’ data accumulates daily, data explosion is no surprise. Companies get help from data collection and analytics to catch up on their sales, customer insights, or brand reputation. However, even though voice data is the most direct feedback businesses receive from customers, they usually overlook its importance.
To better understand how customers evaluate products & services, we explain how to analyze the sentiment in audio files and the top three methods companies can implement.
What is audio sentiment analysis?
Traditional sentiment analysis methods mainly rely on written texts such as reviews, feedback, surveys, etc. However, as human language is complex, nuances such as irony, sarcasm, or intentions are not always easily understood in the written content.
- one of voice
- other frequency-related measures
Figure 1. Raw waveform plots for different emotional states using the same sentence
So, emotions can be better recognized by combining speech tone and written content analysis than by considering only written feedback.
In recent years, companies started implementing audio sentiment analysis methods to understand their customers’ sentiments better and provide them with a better experience.
To avoid premature investments into audio sentiment analysis, we have curated this article so adopters and developers can familiarize themselves with the technology, how it works, and the methods to achieve it.
How does audio sentiment analysis work?
Figure 2. A simplified comparison of written content and multimodal (text + audio) sentiment analysis
3 methods of conducting audio sentiment analysis
There are three main methods of conducting audio sentiment analysis.
1- Automatic Speech Recognition (ASR)
Figure 3. An example of how ASR works
ASR converts speech into text, after which conventional text-based sentiment detection systems are applied. Most companies use the traditional hybrid approach that combines lexicon, acoustic, and language models to predict the outcome.
2- WaveNet (Raw Audio Waveform Analysis)
Generates results directly from the raw audio wave analysis using deep neural networks and considers the context. It is a probabilistic method that offers state-of-art results with a multimodal (text+audio) dataset.
3- Crossmodal Bidirectional Encoder Representations from Transformers (CM-BERT)
Figure 4. The architecture of the CM-BERT network
The CM-BERT approach relies on the interaction between text and audio and dynamically adjusts the weight of words by comparing the information from different modalities.
You can also check our article on sentiment analysis datasets to train algorithms.
Further reading on sentiment analysis
- Top 7 Sentiment Analysis Tools
- Sentiment Analysis Services Benchmarking
- Top 3 MonkeyLearn Alternatives for Sentiment Analysis
For those interested, here is our data-driven list of sentiment analysis services.
If you need any assistance, do not hesitate to contact us:
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