Top 3 Methods for Audio Sentiment Analysis in 2024
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.
The acoustic tone in audio files carries richer information and gives better insights into the sentiments. Sentiment information can be gathered from various voice characteristics, such as
- pitch
- loudness
- one of voice
- other frequency-related measures
Source: Emo-DB
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 learn more about sentiment analysis, you can check our comprehensive article.
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?
Source: Association for Computer Machinery
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)
Source: IEEE
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)
Source: Association for Computer Machinery
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:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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