Given the fast pace of today’s news and social media-dominated world, reputation management has emerged as a key element of public relations.
A company’s reputation is a very important measure of success—it affects everything from customer loyalty to shareholder confidence and corporate brand value. PR professionals are expected to monitor, maintain, and, ideally, protect an organization’s or client’s reputation. This involves staying in touch with public sentiment and anticipating or predicting problems.
Sentiment analysis tracks brand perception so that PR teams can monitor and evaluate the public opinion of the brand, its products, and its services. This information enables them to measure potential reputation risks, review how communications strategies are performing, and drive decision-making to improve brand perception.
In the following sections, we’ll examine AI sentiment analysis in more detail, looking at the advantages of using it, some best practices, and exploring its application in PR.
Understanding AI sentiment analysis
AI sentiment analysis is a powerful tool that uses artificial intelligence to automatically determine the emotional tone behind a piece of text. It goes beyond simple keyword matching, using advanced algorithms that automatically decide whether the words in a given document or message convey a positive, negative, or neutral emotion.
AI sentiment analysis is built on two components: natural language processing (NLP) and machine learning algorithms. NLP gives computer programs the ability to engage with human language, while machine learning algorithms offer the system a way to teach itself, learning from past examples and iterating every time, getting better at what it does. The process tends to go something like this:
- Data collection: Getting text data via social media, news articles, customer reviews, and so on.
- Data preprocessing: Cleaning up and preparing text data for analysis, removing any irrelevant information.
- Feature extraction: Identifying and extracting text features, such as words, phrases, and grammar, relevant to sentiment analysis.
- Model training: Training machine learning algorithms, such as deep learning neural networks, on labeled data, so that they can identify patterns and/or correlations between text features and sentiment labels.
- Sentiment classification: Take observed but unclassified (new) text data that’s been fed to the classifier at training time, and have the classifier automatically label it as having positive, negative, or neutral sentiment.
What are the benefits of AI sentiment analysis?
The benefits of using AI sentiment analysis in PR are numerous:
- It helps PR teams incorporate vast datasets and make sense of them in a way that provides them with a comprehensive and up-to-date view of their brand reputation. It can be used proactively and in real-time to help teams anticipate and prepare for emerging threats to the reputation of the brand or organization they represent.
- AI sentiment analysis is an effective tool for PR practitioners to monitor performance by tracking sentiment before, during, and after a campaign.
- It can be used to study target audiences and give insights to PR teams about the kind of materials, content, and messaging their audience enjoys and is more likely to engage with. This helps tailor messaging and content to resonate more with specific groups, improving overall engagement.
- AI tools can save PR teams time and resources and allow them to focus on higher-level strategy and decision-making by automating sentiment analysis.
Getting started with AI sentiment analysis
With a little preplanning and a definitive goal in place, AI sentiment analysis can be a valuable tool in your PR strategy. Here’s how it works.
Identify your goals and objectives
What do you hope to achieve with sentiment analysis? Are you looking to monitor your brand’s overall reputation? Do you want to track the impact of a specific campaign or identify potential crises? Or maybe you just want to see if customers believe your marketing tagline as “the best cloud server for small businesses”?
Having well-defined goals will help guide your implementation process and ensure that you get the most value from your sentiment analysis efforts.
Choose the right sentiment analysis tools
Having formulated your goals, you’re ready to consider the range of available sentiment analysis tools. You can use a free open-source library or comprehensive enterprise-level platforms.
When evaluating tools, think about accuracy, integration, and price, but also think about your needs. Specifically, pay attention to features such as real-time tracking, multi-language support, and customizable dashboards.
Integrate sentiment analysis into your media monitoring strategy
To maximize the benefits of AI sentiment analysis, it’s crucial to integrate it into your existing media monitoring strategy. This involves integrating sentiment analysis into your normal business and workflow, alongside keyword tracking, manual content analysis, or any other monitoring method you already use.
Using sentiment analysis alongside other monitoring approaches gives you a fuller picture of your brand’s reputation. You’ll be able to identify trends and patterns that may not have been immediately apparent.
Set up alerts and notifications
A major benefit of AI sentiment analysis is the ability to have real-time access to insights, so set up alerts or notifications to give you warnings when there is a significant change in sentiment or when there is an issue.
The best sentiment analysis tools let you build alerts based on specific keywords, sentiment levels, or other inputs. You can then use this information to address negative sentiment before it spirals out of control, or leverage positive sentiment to amplify your brand identity.
Practical applications of AI sentiment analysis
AI sentiment analysis has numerous practical applications in PR:
- Perhaps most important is crisis management where, by keeping a close eye on sentiment across different channels, you’ll identify potential crises as soon as they erupt and respond before they get out of hand. This is especially useful when coupled with omnichannel contact center software that tracks sentiment across multiple customer touch points.
- Sentiment analysis can provide valuable competitive intelligence. By analyzing sentiment around your competitors, you can discern their strengths and weaknesses, track their messaging and positioning, and adjust your strategies accordingly.
- Another key application is for brand monitoring. Tracking sentiment in the media or online over time helps you understand how your brand is viewed, who’s viewing it, and where you can improve. This is where marketing data platforms can be particularly helpful, allowing you to collect and analyze data from multiple sources.
Best practices for using AI sentiment analysis in PR
Following best practices for sentiment analysis with AI will enable you to gain rich, reliable, and actionable insights.
Monitor multiple channels and sources
Don’t focus only on one social media platform or even one broadcast medium, but instead sample diverse online sources (social and news media, forums, blogs, etc.). You’ll get a better grasp on what people are saying about you, and possibly uncover important underlying trend patterns that may not otherwise be observed from just one channel.
Analyze sentiment trends over time
This will help you understand how your brand’s reputation is evolving and narrow down some of the drivers of changing sentiment. Refer to historic data to establish benchmarks, and use these benchmarks to compare sentiment and gain perspective about where you can improve.
Combine sentiment analysis with other metrics (e.g., reach, engagement)
This will help you understand how people feel about your brand, how broadly those sentiments are shared, and how they impact your overall visibility and influence.
Use sentiment insights to inform crisis management and messaging strategies
If you identify a potential crisis through sentiment analysis, use that information to develop a targeted response that addresses the specific concerns and emotions expressed by your audience. Similarly, use sentiment insights to refine your messaging and ensure that it resonates with your target audience’s current attitudes and preferences.
Incorporate sentiment analysis into reporting and measurement
Add sentiment metrics to your list of standard key performance indicators so you can see the full picture of what your PR work is doing for your business or organization. Sentiment metrics can show the value of your work to stakeholders and shape your decisions on future strategies and tactics.
Challenges and limitations of AI sentiment analysis
While AI sentiment analysis is a powerful tool for PR professionals, it’s important to be aware of its challenges and limitations.
Accuracy and reliability of sentiment analysis algorithms
Though these algorithms are ever-evolving, they sometimes trip up on other kinds of language – sarcasm, irony, and slang, for example. Additionally, sentiment can be highly context-dependent and algorithms may miss nuances that are obvious to human readers.
Large data volumes
Another challenge is dealing with the sheer volume and variety of data generated by social media and other online channels. Sentiment analysis tools can help manage this data, but they still need processes by which they can filter and prioritize insights based on relevance.
The need for human oversight and interpretation
AI sentiment analysis does not replace the need for human oversight and interpretation; these tools are never perfectly reliable and should only ever be used in partnership with human analysis.
PR professionals should regularly review sentiment data, combining their industry and organizational expertise with the insights generated by sentiment analysis to contextualize and eventually take action.
The future of AI sentiment analysis in PR
AI sentiment analysis allows PR professionals to constantly monitor their brand’s position in real time and take appropriate steps to mitigate any issues that arise. By using artificial intelligence and natural language processing, PR teams can manage and manipulate large data quantities to diagnose problems and make clear, calculated decisions to boost brand perception.
The coming years will be crucial for the PR industry as it adopts this new style of thinking, with AI sentiment analysis being a distinguishing factor among PR agencies committing to more measurable results.
If you adopt sentiment analysis as part of your PR strategy today and start addressing the issues it picks up on, then you’ll truly be light years ahead of the competition.
