Sentiment analysis with AI. How does it help drive change in business? | AI in business #128

What is sentiment analysis?

Sentiment analysis, also known as opinion mining, is the process of automatically processing large amounts of text to determine whether it expresses positive, negative, or neutral emotions. It relies on natural language processing (NLP), which enables machines to understand human language, and machine learning (ML) – training algorithms on labeled datasets to recognize specific words and expressions indicating a particular sentiment.

The main methods of sentiment analysis:

• rule-based approach – assigning appropriate emotions to keywords based on predefined rules and dictionaries, for example, “great” – positive, “terrible” – negative. It’s quick, but less accurate,
• machine learning approach – it is based on training algorithms on labeled datasets, so they can learn to recognize sentiment based on context. It is more advanced and requires a lot of training data.
• hybrid approach – combining both approaches.

Imagine a clothing company that wants to gather feedback on its new collection from social media, forums, and surveys. Doing this manually would take weeks. With AI and sentiment analysis, it takes minutes. The algorithm assigns a score to each opinion, from -1 to 1, where -1 is very negative, 0 is neutral, and 1 is very positive. This helps the company quickly see which products customers like and which need improvement.

The following outline shows the process of sentiment analysis using AI:

1. Gathering data. In the first step, customer reviews are collected from various sources.
2. Pre-processing. It involves removing special characters, emoticons, HTML tags, etc.
3. Tokenization. It’s breaking down text into individual words or phrases so that artificial intelligence can process textual information more efficiently.
4. Linguistic analysis. Identifying parts of speech, recognizing negation, comparatives, and superlatives, etc.
5. Sentiment classification. A key moment that involves assigning a positive, neutral, or negative label.
6. Results aggregation. This is the calculation of the overall sentiment for a given set of opinions.

Such prepared data serve as an excellent starting point for further analysis and drawing business conclusions. Thanks to the automation of the process, companies can continuously monitor customer sentiments and quickly respond to emerging signals.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Why is sentiment analysis important for businesses?

Tracking what customers say about a brand online is crucial for businesses today. Analyzing hundreds of comments and posts manually is just too much work.

Automated sentiment analysis helps keep an eye on brand mentions in real time and respond quickly. Here are the key uses:

• improving customer service – identifying and responding to negative feedback quickly,
• protecting reputation – continuous monitoring of brand sentiment helps prevent reputational crises,
• market research – tracking trends, benchmarking against competitors, and discovering niches. According to research, 90% of purchase decisions are preceded by online research.
• product development – collecting user feedback and analyzing it for improvements and innovations.

Examples? A restaurant chain can analyze guest reviews on platforms like TripAdvisor to improve the quality of dishes and service. A bank can track sentiment towards a new mobile app to promptly address any issues and tailor features to user needs. A natural cosmetics manufacturer can monitor discussions on forums and Facebook groups to discover a niche for a new product.

Coca-Cola used sentiment analysis to track conversations about the brand on social media during the 2018 FIFA World Cup. This allowed them to adjust their advertising message in real time.

T-Mobile, in turn, thanks to sentiment analysis, identified the main issues of customers and implemented improvements, which resulted in a 73% decrease in complaints.

As you can see, there are practically limitless applications for sentiment analysis. The key is to effectively translate the insights gained into actionable optimization strategies.

How to leverage the results of sentiment analysis obtained with AI?

Sentiment analysis provides valuable insights, but the real value emerges when we translate them into specific actions.

• personalizing customer communication, such as automatically adjusting the chatbot’s tone based on the user’s mood,
• customer segmentation and better matching of offers, as well as identifying the main pain points of users of a given product,
• optimizing marketing campaigns based on emotional reactions to the message,
• quick response to emerging crises and prevention of escalation through immediate intervention,
• improving products and services according to customer expectations expressed in online reviews.

Imagine sentiment analysis shows that customers complain about long wait times on the hotline. By implementing a voicebot to handle some inquiries, you can significantly reduce queues and increase caller satisfaction. If the voicebot software detects that users are praising a new feature in the app, it’s worth leveraging that insight in a product promotion campaign.

Real-time sentiment analysis is a powerful crisis management tool. By catching the first negative signals, you can respond quickly before a crisis escalates. Effective communication and honesty are key – customers appreciate when a company admits a mistake and shows how it plans to fix it.

The key advantage of using AI for sentiment analysis is speed and scale. Manually, we can analyze at most a few hundred opinions. Meanwhile, AI tools can process hundreds of thousands of mentions in minutes, providing an up-to-date picture of the situation. This enables making accurate decisions here and now.

Top AI sentiment analysis tools

There are many tools available on the market that use AI for sentiment analysis. They differ in features, interface, and price. Among the most popular are Brand24, Hootsuite Insights, and Komprehend.

Brand24

Brand24 (https://brand24.pl/) is a Polish tool for internet monitoring and sentiment analysis. It collects mentions from social media, websites, forums, blogs, etc. It automatically labels sentiment as positive, neutral, or negative. It generates reports and statistics regarding the number of mentions and reach.

Brand24 offers a free 14-day trial period, and prices start at 99 PLN/month. It works great for small and medium-sized businesses, especially in e-commerce and services. It stands out for its ease of use and clear reports.

Source: Brand24 (https://brand24.pl/)

Hootsuite Insights

Hootsuite Insights (https://www.hootsuite.com/products/insights) is a powerful tool for social listening. It analyzes data from over 100 million sources in 50 languages, providing detailed insights into sentiment, trends, and benchmarks. Demos are available upon request, with prices tailored to individual needs. It’s great for medium to large companies and integrates seamlessly with major social media platforms.

Source: Hootsuite (https://www.hootsuite.com/products/insights)

Komprehend

Komprehend (https://komprehend.io/sentiment-analysis) is a deep learning-based API for sentiment analysis. It recognizes three sentiment states: positive, neutral, and negative, supporting 14 languages, including Polish. With ready integrations and flexible deployment, it’s a reliable choice. The free plan offers 5000 queries per month, with additional queries priced at $0.0001 each for larger companies. Komprehend is ideal for backend use in apps and chatbots, known for its high-quality analysis proven in competitions like SemEval.

Source: Komprehend (https://komprehend.io/sentiment-analysis)

Choosing the right tool depends on a company’s individual needs and budget. It is worth testing different options and choosing the one that best fits the specifics of your business.

Summary

In the digital age, sentiment analysis has become an indispensable tool in the arsenal of modern businesses. The amount of data generated by users is overwhelming, but artificial intelligence can help. Thanks to advanced algorithms, we can instantly analyze millions of opinions and draw conclusions. This is invaluable knowledge for customer service, marketing, or R&D departments.

The key benefits of using sentiment analysis in business are:

• saving time and resources by automating data processing,
• constant monitoring of customer feedback and immediate response to signals,
• better customer segmentation and tailored offerings,
• optimizing marketing campaigns based on feedback,
• quickly spotting market trends and anticipating changes,
• handling crises better and protecting brand reputation,
• continuously improving products and services to meet customer expectations.

Of course, sentiment analysis is just the beginning. The key is to effectively use the insights it provides. Speed of response and aligning strategies with customer expectations are crucial. Brands that can listen and quickly respond to customer feedback gain a competitive edge. AI provides them with tools to do this efficiently and at scale.

The future of sentiment analysis looks very promising. AI models will enhance accuracy, incorporating contextual analysis and multimodal inputs like images, sound, and video. Awareness of the importance of customer opinions and the role of customer experience will also increase. Businesses investing in AI tools for sentiment analysis now will reap benefits tomorrow with loyal customers, a solid market position, and outstanding products. Let’s not waste this opportunity.

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