What Is Sentiment Analysis in Social Listening?

Sentiment analysis in social listening is determining the emotional state of human conversations. It’s mainly an automated process based on Natural Language Processing (NLP). Sentiment analysis has many applications in business because it generates useful insights.

There’s a vast amount of unstructured data on social media platforms. Through sentiment analysis, businesses can extract useful information on consumers’ perceptions to drive their growth and success. 

This guide discusses sentiment analysis in detail to help you understand its applications, benefits, and challenges.

What is sentiment analysis?

Sentiment analysis, or opinion mining, is analyzing text to get its emotional tone. It is a powerful marketing tool that provides insights into consumers’ perceptions about a brand, its products, or services.

It is an aspect of social listening since it involves tracking a brand’s mentions on online platforms. Sentiment analysis doesn’t focus on the volume of mentions but on the context or motive.

Businesses should consider including sentiment analysis in their social media marketing strategies. That’s because social media provides a large volume of data to mine consumers’ opinions influencing your brand’s reputation. 

Collecting, analyzing, and interpreting user-generated data generates valuable insights into consumers’ needs, preferences, and pain points. It also helps you understand the “why” behind conversations, providing valuable customer feedback about your marketing campaigns, product releases, and customer service.

How does sentiment analysis work?

Sentiment analysis works by analyzing text data and categorizing it as positive, negative, or neutral sentiment.

You can do sentiment analysis manually if you’re analyzing a small data volume.

But automating the process is better, especially when analyzing a lot of unstructured data from numerous sources. 

Most brands use social listening tools to conduct sentiment analysis. These tools apply Natural Language Processing (NLP) to understand human language and determine the emotional tone behind words.

Social listening tools follow these steps to carry out sentiment analysis:

• Monitoring online conversations for brand mentions, relevant keywords, and hashtags
• Breaking down words and phrases into topics and categorizing the sentiments behind them as positive, negative, or neutral
• Calculating their sentiment scores

Sentiment analysis involves three methods (or approaches):

1. Machine learning (ML) model uses data and software algorithms to train machines (AI) to imitate humans. In this ML approach, a machine learns how to identify emotional sentiment from unknown text after being trained repeatedly on known data.
2. Rule-based systems use predetermined rules to identify, classify, and mark keywords based on their sentiment. 
3. Hybrid approach combines machine learning and rule-based systems to determine the sentiment in text data. It is a more complex approach but with higher accuracy and speed than the other two methods.

Types of sentiment analysis 

You can use sentiment analysis for various purposes, depending on your focus. 

Types of sentiment analysis include:

Standard sentiment analysis

It’s the most common type of sentiment analysis. This process involves identifying the polarity (overall sentiment) in a statement or phrase.

You can express polarity as a sentiment score, ranging between -100 (highest negativity) and 100 (highest positivity), where 0 indicates neutrality.

You can also classify the polarity as:

• Positive – For example, “Sprinklr Social is an impressive platform for managing all our social media marketing activity.”
• Negative – For example, “Sprinklr Social is not intuitive and is hard to use/navigate.”
• Neutral – For example, “I am unsure what I dislike about Sprinklr Social.”

Aspect-based sentiment analysis

This type of sentiment analysis focuses on a specific aspect or feature in a text. So it identifies what a person feels or thinks about this feature.

Aspect-based sentiment analysis is useful in product reviews where consumers discuss what they like or dislike about different products. 

Common product aspects in such discussions include price, durability, efficiency, functionality, user-friendliness, and intuitiveness.

Examples:

1. “The softness of this mattress pad is mind-blowing. It adds heavenly comfort to our beds, creating a luxurious sleeping experience that our guests can’t stop talking about.”

Aspects: softness, comfort

Opinion: unparalleled, creating a  luxurious sleeping experience

Sentiment: Positive

2. “Nothing but a thick mattress pad. No extra support.”

Aspect: Thick

Opinion: No extra support

Sentiment: Negative

Fine-grained sentiment analysis

This type of sentiment analysis also focuses on polarity. But it adds more categories to the sentiment scores to classify opinion.

Fine-grained sentiment analysis is similar to 5-star product ratings.

Examples of categories are:

• Positive
• Very positive
• Neutral
• Negative 
• Very negative

Intent detection

Intent-based sentiment analysis helps you understand the reason or action behind a consumer’s opinion. 

You can also identify the person’s position in the purchasing cycle.

So it allows you to spot any issues or problems they’re facing and the intervention you can make to improve customer experience.

For example;

“Disappointed as a Starbucks regular: excessive wait times (40-45 mins for espresso) and focus on fancy drinks over classic coffee. Suggest a dedicated barista for efficient coffee service to honor Starbucks’ original spirit.”

Emotional detection

As the name suggests, this sentiment analysis analyzes a person’s emotional state when writing their opinion on a specific product or service. 

Emotional detection identifies emotions like sadness, joy, fear, regret, and anger based on the choice of words.

For example;

“The battery life is amazing. I use my AirPods often enough and can go about 5 or so days without charging the case.”

Emotions: Happiness, joy, satisfaction

“I did not like the experience I had with customer service. Had some issues getting it to work with Instagram and waited for help through e-mail.”

Emotions: Sadness, dissatisfaction

How do businesses use sentiment analysis?

Businesses use sentiment analysis in various ways, including identifying and solving problems.

Below are use cases of sentiment analysis:

Sentiment analysis in crisis management and brand reputation

Many brands use sentiment analysis to detect and manage crises affecting them. Online discussions can break or make a brand’s reputation. Hence, the business needs to keep tabs on what people say about it, its products, or its services.

Sentiment analysis helps brands interpret conversations on their social channels to spot and fix issues that could threaten their reputation.

For instance, a brand can use a social listening tool to track mentions across social media platforms, websites, forums, news, and review sites. If it notices negative comments about its products, services, or an event, it should respond promptly to prevent the situation from snowballing.

Here’s an example;

A customer didn’t like the prolonged waiting time at a Starbucks store. He also felt that the customer service was below standards. So he called out the brand on X (formerly Twitter). Fortunately, Starbucks spotted the negative comment, responded quickly, and promised to resolve the issue. This prompt response prevented a reputation crisis.

Sentiment analysis in enhancing  customer experience

Some brands use sentiment analysis to streamline their customer service.

A business should understand every customer’s needs and pain points to provide a personalized customer experience. Social platforms are great data sources for valuable customer insights.

A brand’s customer service team can use its social channels to offer customer support. 

For instance, they can use a social media monitoring or listening tool that detects customer queries and sends instant alerts. The tool can also rank customer feedback based on the sentiment score.

If it detects a comment with a high negative score, the tool will label it as urgent and important. So the customer service team, after receiving the notification, will respond promptly to resolve the issue. 

Such an action improves customer experience and satisfaction because they’ll feel that the brand cares about their needs.

Sentiment analysis in product design and improvement

Sentiment analysis is a great way for brands to learn how consumers feel about their products, their benefits, and their features.

A good example is Shopstory, a visual page builder for headless Content Management Systems (CMS). Shopstory uses a social listening tool to monitor relevant online conversations.

The brand gets product development ideas from tracking relevant mentions. It identifies the needs of potential customers and what problems it can solve through its products. 

Shopstory also uses customers’ feedback to learn what features to improve its current products.

Sentiment analysis for market research and competitor analysis

Brands use sentiment analysis in market research and competitor analysis to gain a competitive edge.

Opinion mining reveals gaps, threats, and new opportunities in the market. 

Here’s an example;

Momondo, a travel metasearch site, uses a media monitoring tool to benchmark itself against competitors. The tool helps Momondo keep tabs on competitors’ strategies to pinpoint their strengths and weaknesses. 

For instance, negative comments about a competitor’s marketing campaign present a viable opportunity for Momondo to craft a marketing campaign that resonates with the target audience.

Sentiment analysis also helps Momondo understand how consumers feel about the brand and its position in the market.

The benefits of sentiment analysis

Sentiment analysis presents many benefits to businesses like improving the brand image and customer satisfaction. The information also helps to refine marketing strategies and identify potential influencers.

Sentiment analysis helps improve brand image

Sentiment analysis can help brands improve their public image, which impacts their sales and customer retention.

Most consumers only buy from trustworthy and credible businesses. And they’ll remain loyal to those with a positive brand image. So a brand must maintain a solid image to attract and retain customers.

Opinion mining is a great way for a brand to measure its audience’s overall sentiment. It can gather their perceptions by monitoring brand mentions on social media channels, review sites, forums, news sites, and other platforms.

Insights gained from this analysis can help a brand identify issues that affect its public image. It will also know if the audience is satisfied with the brand’s image. If they have a negative attitude towards it, a brand can work on improving its image by refining its values, mission, and vision.

Coca-Cola is a good example of a brand that has maintained a positive image over the years. One of Coca-Cola’s values is to spread happiness, a slogan that resonates well with many consumers.

It enhances customer satisfaction

Sentiment analysis helps brands improve their customer service, enhancing customer satisfaction.

Customers are satisfied when a brand meets their needs and offers personalized experiences. 

But how can a business know what consumers need? That’s where sentiment analysis comes in.

Sentiment analysis allows brands to identify issues affecting their customers. Most consumers share their frustrations or satisfaction with a product or service on social media, review sites, and forums. So, if a brand uses its social channels to offer customer support, it can analyze discussions on these platforms to measure its efforts.

For instance, a brand can use a sentiment analysis tool to monitor customer queries and rank them according to their urgency and importance. That way, the customer support team can respond quickly to customers’ concerns, enhancing customer satisfaction and reducing churn rates.

Sentiment analysis helps brands refine their marketing strategies 

Sentiment analysis helps brands improve their marketing strategies.

Analytics from sentiment analysis are vital in market research. They allow brands to understand their target audiences’ needs, challenges, behaviors, and pain points. With such an understanding, a brand can segment its audience and craft specific messages that resonate with different segments.

Opinion mining also provides valuable insights into market trends. These analytics help brands understand what is working in the industry and what’s not. It’s also a great way to mine product development ideas.

For instance, after launching a new product, the brand can track relevant mentions to understand the voice of the customer (VoC). Consumers’ perceptions about a product or its features allow brands to know what improvements to make to enhance the product’s features.

It allows brands to identify influencers

Opinion mining is ideal for spotting brand influencers in specific niches or industries.

Analyzing online conversations allows brands to identify influential individuals and thought leaders who genuinely love the brands and their products.

Metrics like their share of voice, number of followers, favorite social media platforms, and posting frequency provide ideal criteria for choosing the best influencers to work with.

These individuals can be the perfect brand advocates since they have a substantial following and can influence their followers’ buying habits.

Is sentiment analysis really accurate?

Sentiment analysis is not 100% accurate since its accuracy depends on some factors.

Since sentiment analysis involves studying, classifying, and categorizing data, it can be a complex process with different outcomes.

Humans and machines can analyze text data to identify the sentiments, opinions, or emotions in the words. Human analysts can manually interpret text at an accuracy rate of 80-85%. But this is tedious and time-consuming.

Sentiment analysis tools automatically analyze large volumes of data in a short time. However, the accuracy depends on the level of the machine’s training. Well-trained tools can have higher accuracy levels of up to 90%. On the other hand, untrained tools barely achieve 80% accuracy levels.

Here are factors affecting the accuracy of sentiment analysis:

• Type of data being analyzed – Is it a large or small volume? Is the data structured or unstructured?
• The language used in the text and its complexity – Is the language formal or informal? Does it contain slang? 
• Type of sentiment analysis method used – Whether manual or automatic, machine learning, or rule-based approaches
• The context of text analysis – On what platform did the data come from? Who is the audience?

Challenges of social media sentiment analysis

We’ve established that sentiment analysis can never be 100% accurate. 

Apart from varying approaches, sentiment analysis faces various challenges that affect its accuracy and efficiency, including: 

Poor data quality

Most of the data processed by sentiment analysis tools is unstructured, making it difficult to process.

For instance, user-generated data on social media platforms is full of misspellings, slang, abbreviations, and emojis. 

Plus, the language used on social media is evolving every day and has different contexts. For example, LinkedIn users use more formal language than those on TiKtok and Snapchat.

Irony and sarcasm detection

Some people love using sarcasm and irony in their speeches.

Here’s an example:

A human analyst can pick out and understand sarcastic words in a text. On the other hand, most machines may not identify sarcasm or irony in words. So they’ll classify the words wrongly during sentiment analysis.

Ambiguity and context-dependence

Online discussions are full of ambiguous and context-dependent words.

Interpreting the sentiment in these words is challenging since most sentiment analysis models are trained using predefined words. 

For example, a statement like, “This company’s customer service is badass,” should be identified as positive. But a model can pick it out as a negative statement if it’s an out-of-dictionary word. 

Also, a word like “Apple” is context-dependent. It could refer to a fruit or the global tech company. So an analyst should first understand the context in which words are used before assigning a sentiment to them.

Negation detection

Negation is the use of contradicting words that change the sentiment in a statement.

For example, “I do not like this movie.” The word ‘not’ changes the statement from positive to negative.

Some sentiment analysis tools may not understand negation in text, so they assign the wrong sentiment.  

Multipolarity 

Multipolarity is when a sentence has more than one sentiment.

For example, “This computer is so good, but I don’t like its word processor.”

The polarity in this sentence can be misleading if the analyst assigns a total sentiment, rather than a sentiment to each aspect. So it will have a neutral polarity since there’s a positive and negative sentiment in the sentence.

FAQs

What is the fundamental purpose of sentiment analysis on socials?

The fundamental purpose of sentiment analysis is to understand emotions, attitudes, or opinions in conversations on social channels.

Sentiment analysis helps brands know what consumers feel about them or their products.

How is sentiment analysis different from social listening?

Sentiment analysis generates quantitative information, while social listening provides qualitative information.

In sentiment analysis, the measuring tool will analyze text and generate a sentiment score to show the emotional tone in a statement. On the other hand, a social listening tool generates comprehensive insights that reveal the motive or reason behind people’s conversations.

What is meant by sentiment analysis?

Sentiment analysis is the process of analyzing text data to identify the emotions, attitudes, or opinions expressed in words.

What is an example of sentiment analysis?

A good example of sentiment analysis is when a brand monitors its social media channels to mine people’s opinions on its new product.

Using a sentiment analysis tool, the brand will detect comments in conversations that mention the brand or the product. These comments will show if consumers like or dislike the product. They can also tell what features they are happy or dissatisfied with. So the brand will generate valuable insights that will influence its future decisions.

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