What is sentiment analysis?

Sentiment analysis is the process of analysing text to determine its tone, categorising it as positive, negative or neutral. It can also assess the emotional subtext of a message, such as whether it sounds friendly or helpful. 

This form of analysis helps businesses ensure their messaging is consistent with their brand voice. For example, if a company aims for a casual and familiar tone, a sentiment analysis tool can evaluate whether their communications reflect this style.

Sentiment analysis can be used to analyse various digital text formats, including:

• Emails
• Social media posts, comments and replies
• Chatbot transcripts
• Customer reviews
• Employee survey feedback 

The different forms of sentiment analysis

There are several types of sentiment analysis, each designed to analyse different aspects of sentiment. Here are some of the most common approaches:

• Fine-grained: This method uses a sentiment rating scale (e.g., 1 to 5 or 1 to 10 on a Likert scale) to measure sentiment intensity. After analysing a text, the tool assigns a numerical score reflecting how positive or negative the sentiment is.
• Emotion-based: For businesses needing deeper insights beyond positive or negative sentiment, this approach identifies specific emotions such as happiness, anger, sadness or embarrassment.
• Intent-based: This method helps determine the underlying motivation behind customer sentiment. It can categorise intent as a complaint, criticism, request for information or other key customer interactions.
• Aspect-based: This approach assigns sentiment to specific elements within a text rather than analysing it as a whole. For example, in the sentence: ‘During my stay at the hotel, I was disappointed with the catering options available.’ Aspect-based sentiment analysis would identify ‘catering options’ as the focus of the negative sentiment, rather than applying it to the entire statement.

How do AI sentiment analysis tools work?

Most sentiment analysis tools on the market use generative AI to analyse digital text. AI sentiment analysis tools are trained on rules-based lexicons. These rules identify when a word or phrase is positive, negative and so on. AI sentiment analysis can also consider the entire context of a text and attribute sentiments accordingly. 

Because of their versatility, sentiment analysis tools can have numerous applications. Let us consider some of the benefits these tools can bring to businesses.

Why is sentiment analysis important?

Sentiment analysis tools play a crucial role in understanding and improving brand perception. They can help businesses by:

• Analysing sentiment across large datasets
• Providing instant insights into customer and employee sentiment
• Helping businesses build better products more quickly
• Delivering objective insights into brand messaging, free from human bias
• Shaping brand identity, whether a company aims for a positive, optimistic or authoritative tone

One major advantage of AI-powered sentiment analysis is the ability to monitor and track brand reputation in real time. In the next sections, we will explore how businesses can use sentiment analysis for these purposes.

Using sentiment analysis for customer opinion insights

Customer opinions appear across various platforms, including social media posts, product reviews, chatbot conversations, news articles, blogs and online forums.

There are different tools available that can analyse the sentiment of customer opinions. Brand and social media monitoring tools can be used to track brand mentions across different channels. They can also be used to track sentiment regarding mentions on these channels over time. 

Since different customer segments engage on different platforms, multi-channel sentiment analysis can provide valuable insights into how various audiences perceive a brand, product or service. By comparing sentiment across platforms, businesses can gain a more nuanced understanding of customer preferences and expectations.

Reacting in real-time to customer feedback

Sentiment analysis allows businesses to identify and respond quickly to customer feedback, particularly on social media. Addressing negative comments or complaints in real time can make all the difference in preserving positive perceptions of a company’s brand.

Beyond reputation management, sentiment analysis also helps businesses understand which products or services customers respond most positively to. This enables companies to focus on developing offerings that align with customer preferences, ultimately driving stronger engagement and loyalty.

Using sentiment analysis for employee feedback

Sentiment analysis can help analyse employee sentiment in survey feedback. This can help businesses identify positive or negative trends in specific areas of employee satisfaction and experience.

For example, these tools can highlight where employees are positive about wellness, learning and development or their working environment. If there is any negative feedback from employees, businesses can identify it quickly and work to address it. 

The challenges of AI sentiment analysis

While AI-powered sentiment analysis offers many advantages, it’s important to consider its limitations and challenges. Following best practices for AI implementation can help mitigate potential issues.

Potentially inaccurate results

AI sentiment analysis may not always be 100% accurate. For example, an AI emotion-based sentiment analysis tool may not pick up on subtler emotions or sarcasm. Sentences with mixed emotional cues may also be difficult for AI tools to classify correctly. Other reasons why AI sentiment analysis tools can provide inaccurate results include:

• Language complexities: The text may contain several different languages
• Different meanings of words or phrases, depending on linguistic context linguistic or cultural context
• Biases in AI training data: If an AI model is trained on biased or unbalanced datasets, certain perspectives may be favoured

Data privacy concerns

Sentiment analysis involves processing large sets of personal data from customers or employees, which can raise data privacy concerns. Businesses using these tools should be mindful of UK data protection laws, such as the Data Protection Act and UK GDPR. For detailed requirements, consult official government guidance.

AI sentiment analysis best practices

To mitigate potential issues and enhance accuracy, businesses should follow these best practices when using AI sentiment analysis tools.

Encourage human supervision of AI

AI sentiment analysis tools can struggle with nuanced emotions, such as sarcasm, cultural idioms or ambiguous phrasing. Human oversight helps ensure more accurate interpretations, particularly in complex cases.

When determining whether a sentiment analysis result requires human review, consider:

• Use of slang or cultural idioms that may not translate accurately
• Presence of emojis, especially those with multiple meanings depending on context
• Sarcasm, double negatives or ambiguous phrasing, which AI may misinterpret

Feed the tools clean data

Feeding AI tools ‘clean data’ can provide more useful and accurate feedback. You can achieve this by removing any irrelevant information from the digital text and ensuring the text’s language adheres to standard grammar and spelling rules. 

Our guide to people analytics illustrates how ‘good data’ provides the best results. Good data means it is:

• Accurate
• Reliable
• Complete
• Timely
• Relevant

Training employees in data literacy and AI skills can help ensure sentiment AI tools are only fed clean or ‘good’ data. This may involve:

• Collection of data through spreadsheet tools
• Teaching of common data terminology
• Basic training in data mining and analysis fundamentals

Consider the environmental impacts

Businesses might also consider the environmental impacts that AI can have on sustainability.  AI is known to consume considerable amounts of energy to make calculations. Investing in AI technology that draws on renewable energy sources can help meet sustainability goals. Some ways to use AI sentiment analysis tools more sustainably include:

• Using small pre-trained language models that do not require extra energy for additional training
• Taking an efficiency-first approach to using AI by training it on the best possible data
• Researching generative AI that uses energy-efficient algorithms

Sentiment analysis can provide business leaders with insights into brand reputation and customers’ perceptions of their products. It can also be used to analyse employee feedback in surveys, revealing how employees feel about learning and development, perks, flexible working and more. Some of the most common sentiment analysis tools on the market are driven by AI. Therefore, it is useful to train staff in AI best practices before using them.