In the realm of AI, understanding human emotions has become paramount. "Emotional Analysis" stands at the forefront of this evolution. This article breaks down its essence, workings, implementation, and applications.
What is emotional analysis?
Emotional Analysis includes sentiment analysis and emotion analysis. It is the AI-driven process of detecting, interpreting, and responding to human emotions and sentiments.
What it’s not, is a mystical mind-reading tool. It's a data-driven approach that seeks to understand the emotional undertones present in various forms of human expression.
Why is it important?
In a world dominated by online interactions, understanding the emotional context can be the difference between success and failure.
If you can understand what your customers are not just saying about you, but are feeling about you online, you can have a much deeper understanding of what you should do next to serve them better.
Whether it's customer feedback, social media comments, or video reactions, Emotional Analysis provides a deeper layer of understanding, ensuring businesses respond effectively and empathetically.
How Does Emotional Analysis Work?
At its core, Emotional Analysis employs advanced algorithms and machine learning models. Here's a simplified breakdown:
1. Data Collection
The first step involves gathering data, which could be text from social media, voice recordings from customer service calls, or even facial expressions from videos.
In the case of processing text data, you should first organize your data into a CSV or other standard format, and upload into a text processing program such as Lettria.
2. Data Processing
You then need to clean and organize your data, making it ready for analysis. This is important because if your data isn’t cleaned, it can significantly affect the quality of the end result.
Data cleaning includes tasks like removing irregularities, invalid characters, duplicate content, structural errors or unwanted outliers, all of which can cause your final output to be lower in quality and accuracy.
3. Analysis and Interpretation
Using trained AI models, the data is then analyzed to detect emotional cues.
These could range from keywords in text that denote happiness or frustration, to voice modulations that indicate stress, or facial expressions that reveal joy.
With Lettria, you can easily perform sentiment as well as emotion analysis on your text data, understanding which ones out of 20 different emotions are being expressed, or how much positive or negative sentiment is being expressed in the text.
4. Reporting and Aggregation
Post-analysis, the AI provides insights, which are often as emotions categorized as positive, negative, or neutral, giving a deeper understanding of the emotional context.
When you have insights and results for thousands of data items, the next final step would be to aggregate them using a business intelligence tool like PowerBI or Tableau, to gain valuable insights.