With the emergence of platforms like YouTube and TikTok that provided new spaces for creators to monetise their products or services, the creator economy evolved and has grown to such an extent that it is impacting the broader economy, marketing, and technology.

SENTIMENT ANALYSIS

Sentiment analysis, also known as emotion AI, is a technique in natural language processing (NLP) that identifies and categorises opinions and feelings expressed in text—such as customer reviews, social media posts, or survey responses—to determine whether the sentiment behind the text is positive, negative, or neutral.

Sentiment analysis gives organisations insight into customer opinions about their products and services, enabling them to improve and enhance their overall customer experience. It benefits marketing, customer service, and brand reputation management, where understanding public perception is critical.

How Sentiment Analysis Works.

Several key steps are necessary if emotions and opinions expressed in text are to be analysed effectively. These steps include:

1.     Text Preprocessing. This is cleaning the text by removing unnecessary punctuation, special characters, and stop words. Stop words are words that do not carry any meaning on their own, such as “and”, “the”, “of.”

Text preprocessing also involves breaking down the text into smaller chunks or phrases and reducing words to their base or root form—a process known as stemming and lemmatisation.

2.     Feature Extraction. In this step, the text data is converted into numerical features that can be used for analysis. Common techniques include:

·       Bag of Words (BoW), where text is represented as a collection of its words.

·       Word Embeddings. This is where neural network-based algorithms like Word2Vec or GloVe are used to capture semantic meaning.

3.     Model Building. In this step, a machine learning model is chosen and trained to classify the sentiment. Simpler models like Naïve Bayes or SVM use statistical models. Deep learning models such as LSTM (Long-Short-Term Memory) are recurrent neural networks designed to handle and process natural language and can classify emotions such as anger, joy, sadness, or aspects like product features.

4.     Once the model has been trained, it can classify text into sentiment categories such as positive, negative or neutral.

5.     It is essential to evaluate the accuracy and precision of the data collected, share insights with stakeholders, and continuously improve based on feedback and new data.

The benefits of sentiment analysis for businesses

Applying sentiment analysis in your business can provide valuable insights into customer opinions, improve decision-making, and enhance overall customer experience. Some practical applications include:

1. Customer Feedback Analysis

Sentiment analysis enables businesses to monitor reviews or survey responses on platforms like Google, Yelp, or product review sites. It helps understand customer satisfaction levels and identify the key pain points or areas for improvement that businesses should consider.

2. Brand Monitoring

Analysing sentiment on social media mentions (Twitter, Instagram, etc.) or blog posts enables companies to track how their brand is perceived online. They can measure brand sentiment over time and assess the impact of marketing campaigns, PR crises, or new product launches.

3. Competitor Analysis

Sentiment analysis can help businesses understand how consumers feel about competitors. Tracking sentiment for competing brands can reveal areas where a company can improve their product or services to become more competitive.

4. Market Research and product improvement

Analysing sentiment around competitors and broader industry trends can provide valuable insight into market dynamics and consumer preferences, which are essential information when considering marketing strategies.

Customer feedback on product features such as design or specific components assists businesses in understanding which features need further development and innovation to meet customers’ needs.

5. Crisis Management

When a brand faces negative press or a crisis, sentiment analysis can alert businesses to a spike in negative sentiment, enabling them to respond quickly with corrective actions and manage the damage to their reputation.

While sentiment analysis provides valuable business insights, it has risks and challenges. Understanding these risks is crucial for effectively making informed decisions and using sentiment analysis.

Applying sentiment analysis in your business can provide valuable insights into customer opinions, improve decision-making, and enhance overall customer experience

The challenges to consider when using sentiment analysis

Several challenges must be kept in mind when using sentiment analysis. These are:

1.     Inaccuracy in interpreting the Sentiment.

There are three areas where this is particularly relevant.

·       When sarcasm and Irony are used. Sentiment analysis often struggles to detect sarcasm, irony, or nuanced language, leading to incorrect sentiment classification. For example, the sentence “Oh great, another update that crashes my phone!” might be incorrectly labelled as positive when it is negative.

·       Contextual Understanding.  Sentiment analysis models may fail to grasp the context of a sentence or text. Words that are positive in one context can be negative in another. For instance, “light” could be positive for a laptop’s weight but negative when describing its performance.

·       Ambiguity. Some statements are inherently ambiguous, making it hard to assign clear sentiment. For example, “It works fine for now” could be neutral or carry hidden dissatisfaction.

2. Cultural and Linguistic Bias

Different languages, dialects, or slang can affect the accuracy of sentiment analysis. A tool trained primarily in English might not perform well when analysing non-English languages or regional variations. This may lead to misinterpretations. Likewise, sentiment analysis systems may not fully capture the subtleties of different cultures, potentially leading to misunderstandings in a global market.

3. Over-reliance on Automation

Sentiment analysis works most effectively when there is human oversight and input. Where businesses rely solely on sentiment analysis tools without human oversight, it can lead to poor or incorrect decision-making. Sentiment analysis is not infallible and can miss important nuances that only human judgment can capture.

Sentiment analysis results must be considered part of a broader data set rather than the sole factor driving strategic actions.

4. Data Quality and Bias

Like any automated system, the quality and integrity of the results depend on the quality and accuracy of the data being entered. If the text preprocessing is insufficient and there is still “noise” in the data, such as irrelevant comments, spam or off-topic discussions, this can skew the results and lead to inaccurate sentiment assessments.

Likewise, if the algorithm has been trained on biased or incomplete datasets, it can produce skewed results. For example, if the training data is more representative of a particular demographic or user base, the sentiment analysis might be less accurate for other groups.

5. Privacy and Ethical Concerns

Sentiment analysis often involves analysing large amounts of text data from social media, reviews, or emails, raising privacy concerns. Businesses must ensure that they use data responsibly and comply with privacy regulations.

If businesses misuse sentiment analysis to manipulate public opinion or present an overly favourable view of their brand by downplaying negative feedback, this can lead to ethical issues and damage trust.

6. Limited Emotion Detection

It must always be remembered that while sentiment analysis typically focuses on broad categories like positive, negative, and neutral, it may not capture specific emotions (e.g., joy, anger, frustration) that are driving the sentiment. A negative sentiment doesn’t reveal whether it’s mild disappointment or outright anger, which can be crucial for customer service.

How to manage the challenges

Despite these challenges, sentiment analysis remains a valuable tool for businesses, and businesses can mitigate the risks in various ways.

1.     Combine Sentiment with Human Analysis

As mentioned above, over-reliance on sentiment analysis can lead to poor or incorrect decision-making. Automated sentiment analysis must be combined with human review and oversight to ensure a more nuanced and accurate interpretation of the data.

2.     Custom Models and Industry Tuning

Sentiment analysis models trained in one domain, such as general product reviews, may not perform well in another domain, such as legal or technical. Hence, it is essential to tailor sentiment models to fit specific industries or domains to improve accuracy and ensure the model is trained on relevant data to capture the right sentiments and emotions.

3.     Monitor Bias and Continuously Train Models

It is essential to periodically review the data used to train sentiment models to reduce the risk of bias. Models should also be continuously updated to adapt to new language trends, cultural shifts, and user feedback.

4.     Transparency and Ethical Use

One of the challenges with using sentiment analysis is privacy and ethical concerns. Therefore, it is essential to ensure transparency in how sentiment data is collected and analysed and handle customer data ethically to avoid privacy violations.

By recognising these risks and taking proactive steps, businesses can leverage sentiment analysis more effectively and responsibly.

Sentiment analysis will continue to be an essential tool for businesses, particularly as further developments in AI and machine learning (ML) enable sentiment analysis models to be more adept at understanding context, idiomatic expressions, and cultural nuances.

With the rise of real-time data from social media and other platforms, sentiment analysis will continue to evolve. This will enable businesses to respond quickly to customer feedback and market trends, monitor brand health, and make data-driven decisions to meet customer preferences.

With the arrival of ChatGPT later in 2022, the metaverse bubble burst, resulting in financial losses for companies that had invested heavily in it.

The marketing landscape continues to change and evolve rapidly due to technological advances, evolving consumer behaviours and shifts within the marketing industry.

Many non-profit organisations find it increasingly challenging to have their message heard by the wider community and potential donors and funders. The impact of technology, which once enabled organisations to amplify the good news stories and the change they brought to clients, seems to be decreasing as people scroll past the good news stories.

2024 presented challenges for not-for-profit organisations communicating their message. These challenges centred around the organisation’s ability to keep up with technological trends, societal shifts and audience behaviour. It is essential to review the challenges of 2024 and then consider how organisations can improve how they communicate with the broader community in 2025 to ensure their message has the potential to be heard.

The impact of technology in 2024

There were several trends in technology in 2024 that impacted non-profit organisations.

New Technologies

In 2024, the influence and use of AI and automation has become more mainstream for many individuals and for-profit organisations. However, many non-profit organisations, particularly small to medium-sized organisations, have struggled implementing AI due to a lack of resources, time pressure, and uncertainty about balancing AI use with maintaining confidentiality and privacy.

Rapidly changing technologies require financial and time investment to set up and check systems. The majority of non-profit organisations lack the financial resources needed and are time-poor. This means that many non-profit organisations are using older technology that limits what can be achieved regarding marketing and messaging.

Social Media Challenges

These challenges include:

●      Algorithm Biases. Social media platforms prioritise engaging viral content, often sidelining non-profit messages that focus on serious or more complex issues.

●      Pay-to-Play Environment. Organic reach has continued to be challenging on platforms like Facebook and Instagram. The alternative to organic reach is paid advertising. However, many non-profit organisations are reluctant to pay for advertising, particularly when facing budget uncertainty.

●      Platform Fragmentation. With established platforms like X losing relevance and the emergence of new platforms like Threads and BeReal, non-profit organisations must continually adapt to these new platforms and how they can be used to reach the broader community.

Digital overload and declining attention spans

Throughout 2024, people have been continually inundated with messages from various sources. It is estimated that in 2024 a person has processed around 100,000 words per day made up of social media, emails, news, ads, streaming services, and work-related information.

The result is information fatigue, difficulty focusing on meaningful content and a preference for bite-sized, easily digestible content, preferably under 30 seconds. This makes it challenging for many non-profit organisations to convey meaningful messages quickly.

Societal Shifts

Societal shifts have included

Cost of living pressures.

For many people, rising cost-of-living pressures in 2024 reduced disposable income, leading to donor fatigue and lower levels of engagement by community members.

This, in turn, impacts donations.

Misinformation and scepticism

With the rise of social media, there seems to have been an increase in false and misinformation. When the misinformation concerns social issues, it can undermine the credibility of legitimate not-for-profit organisations and dilute public understanding or support for their work.

False and misleading information can increase scepticism within the community, which can be a barrier to the message of non-profit organisations.

Audience Behaviour

Changes to audience behaviour include generational shifts and balancing the messaging requirements of different generations. For example, Gen Zs need authentic, values-driven communication that is quick and impactful. Traditional methods, such as lengthy reports that may reach Baby Boomers, are not ineffective with Gen Zs.

Tailoring messages that resonate across generations is an ongoing challenge for many non-profit organisations.

The other aspect of the audience’s behaviour is fatigue, and there are two aspects to this.

Ask Fatigue

As mentioned under society shifts, many people feel donor fatigue with increased cost-of-living and constant fundraising appeals from multiple organisations. However, another form of fatigue also impacts non-profit organisations.

Crisis Fatigue

With the ongoing prevalence of crises, such as climate emergencies, the ongoing impact of COVID and the increase of regional conflicts, many within the community feel desensitised or overwhelmed. There is also a sense that problems are too complex. These feelings reduce people’s willingness to be engaged and assist in an ongoing capacity.

Given these challenges, what steps can non-profit organisations take in 2025 to give themselves the best possible opportunity to have their message heard?

It is estimated that in 2024 a person has processed around 100,000 words per day made up of social media, emails, news, ads, streaming services, and work-related information, resulting in information fatigue.

Optimising communication in 2025

Not-for-profit organisations can take several steps to ensure they optimise their communications in 2025 despite the challenges and changes that emerged in 2024.

Embrace and leverage technology.

Many not-for-profit organisations avoid technology and staying updated with emerging platforms or trends, such as short-form videos, and using AI to create interactive experiences for people to watch. The result is that their message is not being heard or is being ignored because of the impact of other messages.

Part of this avoidance is due to financial and time constraints; however, a large part of the issue is the leaders’ mindset. Leaders and Management boards often do not understand the value and importance of embracing and using technology effectively. When leaders do get enthusiastic about the possibility of using technology effectively, the enthusiasm is often short-lived. Hence, it never becomes a consistent priority that achieves positive results over the long term.

When leaders begin to understand the potential impact technology can achieve in getting the organisation’s message out, we begin to see the following shifts within an organisation.

1.      There is a budget allocation for social media and technology use.

2.      There is a requirement for the CEO to report to the Board on the effectiveness of the budget allocation in getting the organisation’s message out into the community.

3.      SEO and content marketing are prioritised and optimised for voice and AI-powered search tools to improve discoverability.

4.      Technological innovation is encouraged, such as using AI-powered chatbots for instant communication and FAQs on websites and social media platforms.

The priority is the personal.

The days when sending out generic information was acceptable have long gone. One of the changes that has occurred in marketing is that audiences expect that if an organisation is going to market to them, it will have done enough research and collected enough data to understand its audience’s preferences, behaviour, and demographics so that there is a more targeted outreach.

This expectation extends to not-for-profit organisations. If NFPs market to potential donors, current donors, and/or community members, they must have enough data to understand their targets’ preferences, behaviour, and demographics.

In other words, the messaging and marketing must be personalised to the people they are targeting.

For example, if the organisation is going to target Gen Zs and Millennials, it will need to develop a completely different message from the one it would use for Baby Boomers. To effectively target Gen Zs and Millennials, an organisation needs to demonstrate the impact of their work on social justice, sustainability, or other causes relevant to young people in these generations.

Another area where personalisation is crucial is accessibility and inclusion. Content must be accessible to people with disabilities, such as screen-reader-friendly video captions. Translation tools are also essential when communicating effectively with diverse communities.

The importance of impact

What is the impact of what the organisation does? Can the impact be measured?  Can the impact be measured in dollar figures?

Many organisations, particularly small to medium-sized not-for-profit organisations, struggle to clarify their service’s impact and how to measure it effectively. Becoming clear about the impact the organisation is having and communicating it effectively with compelling stories that demonstrate the impact in practical ways is a powerful way to build trust within the broader community.

The essential factor is trust.

Improving communication in 2025 is about building trust.

Trust is built over time in personal relationships. The more you get to know a person, the more time you spend with them, the greater the possibility of trust developing.

Trust is also built when the other person relates to you in a personal way. When someone relates to you in general, non-specific ways, it is harder to build trust, just as it is challenging to build trust when the other person has a negative impact on us.

The same principles apply to getting your message heard in 2025.

●      You need to use and leverage technology consistently. The importance of SEO cannot be stressed enough, particularly SEO optimised for AI power search tools. SEO allows your message to be found, and trust develops when the messaging is consistent. Many not-for-profits post on an ad hoc basis. The lack of consistency negatively impacts the organisation.

●      The importance of the personal. The challenge with messaging using social media and technology is that we must clearly demonstrate in our posts that we understand our audience. If people don’t feel understood, they will keep scrolling, and our message will be lost.

●      Share your impact in a personal way. Impact has two aspects. On the one hand, we must clearly articulate the organisation’s impact on the community. The second aspect of impact is the consistency with which we repeat the message. The clearer we can get the message, and the more frequently we repeat that message, the more impact we will have.

As we improve our communication tactics, more people will trust our message and what the organisation is achieving.

By thinking creatively and developing a consistent, coherent communication strategy, not-for-profit organisations can continue to make meaningful connections to their communities and audiences in 2025.

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