The success or failure of a brand depends not only on sales but also on the opinions of customers. It’s very important to understand what clients and potential customers think about your brand, whether they have already purchased a product/service or not.
Does the brand fit in with current trends? Is the target audience’s perception of the brand positive or negative? These are questions that any company should ask themselves regularly.
The word sentiment refers to feeling or sensation. Sentiment analysis is performed to determine how a target audience welcomes and perceives a brand. Even stock market experts use sentiment analysis to estimate changes in stock prices based on buying behavior and the overall sentiment of stock investors.
What is sentiment analysis?
Sentiment analysis includes evaluating emotions, attitudes and opinions. Companies and brands use this method to obtain information to understand a customer’s response to a particular product or service.
Sentiment analysis tools use advanced artificial intelligence technologies such as natural language processing, machine learning techniques, text analysis and data science to identify, extract and study subjective information. Basically, it’s used to classify text as positive, negative or neutral.
Traditional KPIs such as views, shares, clicks, likes, comments or clicks focus on the number. Sentiment analysis goes beyond quantity because it focuses on the quality of interaction between the audience and the brand.
What is customer sentiment analysis?
If customers are not satisfied with something, they usually express it through comments recorded in customer satisfaction surveys.
This type of survey consists of different types of questions, such as multiple-choice questions or net promoter score questions. However, when researchers need to collect detailed feedback from clients, they often ask open questions.
If there are too many negative words in the questionnaire, the company can take action in order to solve a customer’s problem. Customer sentiment analysis is useful to answer the following types of questions:
- Do customers like our products and services?
- Are customers dissatisfied with our products and services? Why?
- Has the number of negative reactions gradually increased?
- Which brand product received the most positive responses?
- Have we received too many negative responses recently?
- Compared with the previous quarter, did the number of positive, neutral and negative reactions remain the same?
- Has the extent of positive or negative reaction changed?
By conducting customer sentiment analysis, brands can discover whether they are heading in the right direction, measure customer satisfaction levels and reduce mistakes to improve the quality of their products or services.
Why is sentiment analysis so important?
The increasing value of sentiment analysis is due to its proven effectiveness for companies and brands around the world. It’s particularly useful for conducting market research and for monitoring the presence of brands on the Internet and different social networks.
Sentiment analysis techniques and programs applied to network monitoring tools and social media represent a faster and more effective way of determining what customers think and feel about a brand.
Given the growing role of the internet in all areas and the difficulty of monitoring it, it’s no surprise that the field of use of sentiment analysis tools has been extended to different areas of companies and organizations, more intensively in marketing and communication.
Benefits of customer sentiment analysis
Here are some of the main pros of performing sentiment analysis:
- It keeps you updated on what consumers like and dislike about your products and services.
- It allows you to focus on different market segments and create products and services that are appealing to specific audiences.
- Sentiment analysis tools allow your team to get involved in product development collaboration, which can motivate them and improve their performance.
- It supports the analysis of reviews or customer feedback and compares them with competitors to identify areas of improvement.
- Sentiment analysis can help you solve problems that customers encounter when using your services or products.
- It allows you to understand the mood of your workers through surveys. In this way you can address their concerns by making them feel that they are heard and valued.
- It benefits the reputation of your brand and the perception of your customers.
- It can help you understand the emotions of your customers and anything that influences their opinions.
When should sentiment analysis be done?
Sentiment analysis is particularly important in advertising campaigns on social networks, because it’s where potential customers will immediately react to the brand’s posts and even interact with each other, which is usually done in a sincere and direct way. If the comments indicate a negative or wrong perception of the products or services, brands can quickly adjust and re-evaluate specific aspects after the analysis.
Also, when a new version of a product is launched or a brand makes visual changes to its image, sentiment analysis is very useful to assess the impact of the change on customer satisfaction and potential customer behavior.
Technically, it’s quite easy to analyze large amounts of text, but the key is to distinguish between relevant and irrelevant text. This is not only about filtering spam, but also about excluding text that is indirectly related to the product that is being analyzed.
In the relevant reviews about the brand itself, you must filter and divide the content. For example, based on whether the text is really about a product review, or whether the criticism is aimed at customer service or packaging. There’s no doubt that this type of criticism is also very useful, but if it’s evaluated together with the pure evaluation of the product, it doesn’t really contribute to the analysis. Sentiment analysis is also useful for measuring the success of marketing campaigns.
How does sentiment analysis work?
Through sentiment analysis, you can understand the exact intent of a phrase and know whether the phrase refers to a brand, a specific product or any other aspect (such as customer service).
Then you have to look at the value of the phrase, and to do this, you have to apply the sentiment polarity technique, by which the message can be categorized according to the user’s intention when it was written. The message can be positive, neutral or negative.
There are two ways to process the information obtained:
1. Manual sentiment analysis
It usually occurs when the keywords for which you want to collect information are highly influenced by current events, or may represent different meanings in different contexts. This means that you must classify everything carefully in order to avoid confusing the data. For example, the well-known Philadelphia cheese brand is named after an American city, so you would probably get a lot of data that is not related to what you are looking for.
2. Automatic sentiment analysis
In this case, a series of keywords will be marked to automatically classify or discard any text containing the word or a combination of them based on certain parameters that have been marked directly. For example, messages containing “dislike”, “bad quality” or “I don’t recommend it” will be automatically classified as negative data. On the other hand, messages that contain “I love”, “amazing” or “delicious” will be classified as positive.
How is machine learning used in sentiment analysis?
The term machine learning refers to the combination of scientific disciplines that create systems that can learn without human intervention. A single person or team cannot analyze a huge amount of data to draw conclusions, but algorithms can surely detect behavior patterns.
In marketing, algorithms are usually implemented in social media measurement tools. These tools use binary trees to correctly construct data and tree behavior patterns (positive, neutral and negative).
With this structure, behavior can be observed, and when a large amount of data has been collected, the algorithm provides a certain percentage of the probability of predicting one of the three mentioned behaviors.
How to convert data into useful information
The process of converting data into information that is truly useful to the company goes like this:
- Data filtering: Here, keywords are used to discard unwanted content. Then, specific words are established to generate categories according to their origin or polarity.
- Content extraction: You begin working with high-quality content.
- Content analysis: This process can be done by algorithms or by people. Here, useful and high-quality content will be included in its corresponding category.
- Content removal: Some content may have been incorrectly categorized and this is when we can move it to the correct category or discard it altogether.
- Review: This stage is used to detect whether a word that is considered positive is used negatively at some point.
Types of sentiment analysis
One of the most important things in sentiment analysis is to structure and classify sentiment types. There are many possibilities to classify the feeling of a text and each tool or work team can use a different one. Therefore, it’s very important to choose the right product for you.
This is the most common analysis and consists of classifying positive, negative or neutral opinions. In some cases, to make it even more specific, some may include “very positive” and “very negative” categories. Another common practice is to ignore the neutral category and treat all texts as positive or negative.
This type of classification focuses on what users intend to do with what they say, not exactly what they say. It usually has the following tags: interested/not interested.
This is probably the most specific classification because it focuses specifically on the emotion that is transmitted. The most common ones are: disappointment, joy, frustration, anger or sadness. This type of sentiment ranking is widely used in product launches or campaigns in order to measure how aligned expectations are regarding the audience’s opinion.