The Ultimate Guide to Sentiment and Emotion Analysis

Sentiment and emotion analysis enables businesses of all sizes to keep track of how customers feel about their products and services. This ultimate guide dives into the how the insights gained from understanding sentiment and emotion can deepen customer relationships and loyalty.

As companies seek out new ways to capture buyers in a rapidly shifting global market, the importance of understanding what people are thinking continues to grow.

Reducing a vast, growing sea of consumer opinions into actionable information can seem an insurmountable task, especially when hazy concepts such as sentiment and emotion enter the picture.

Yet, these related concepts tend to take precedence in predicting consumer behavior, especially where it pertains to buying.

In an article for Entrepreneur, Jeff Shore describes the so-called “buyer formula” as follows:

“Current Dissatisfaction x Future Promise > Cost + Fear”

Emotions make an appearance twice in this formula, but where does sentiment come in?

Much like customer satisfaction, sentiment can crop up right after a purchase has been made. However, it can also come into the picture before a customer has bought anything from you, during interactions with your customer services reps or even unprovoked on social media.

A full 54% of consumers placed purchases for products they first learned about on social media. Sleuthing out sentiment wherever it occurs is just as important as assessing and acting on it.

Discovering how sentiment stacks up for or against your brand can be a game changer, allowing you to quickly pivot business operations in a more customer-friendly direction. But, sentiment and emotion cannot be effectively harnessed without first being well understood.

Defining Sentiment Analysis

At its core, sentiment analysis is a process used to learn more about a person’s overall opinion or perception of a brand, product or service, a promotion, or even a specific interaction with a business.

Normally, when referring to sentiment analysis as it is implemented, feelings and affective states are deduced from textual or spoken words. This process is often handled without much context (no knowledge of individual identity, purpose of communication, etc.).

However, more advanced approaches have begun to leverage a variety of in-house and third-party data sources to put assessed conversations in perspective.

With or without explicit contextual information, sentiment analysis can yield highly actionable results for businesses of all sizes.

How Sentiment Analysis Works

Sentiment analysis merges multiple disciplines into one to capture more accurate information about an individual’s feelings. Among these are natural language processing and biometrics, both of which make processing even spoken words possible at speed.

Unfortunately, sentiment analysis is not a perfect solution for sleuthing out consumers’ feelings about an organization. It remains difficult for most sentiment analysis engines to parse complex colloquial phrases as well as tricky figures of speech like sarcasm and negations.

Complexities such as these have kept researchers in the space busy for quite some time.

The Science of Sentiment Analysis

As a science, sentiment analysis tends to progress along one of two roads: rules-based or AI-enhanced.

A rules-based approach to sentiment analysis leverages human decision making to predefine the standards by which information must be judged.

In contrast, an AI-enhanced approach to sentiment analysis uses machine learning models to optimize the process and produce acceptably accurate results from unmoderated information. Differences between these approaches go deeper than the above, though.

Sentiment Analysis Using Rules

Statistical natural language processing is the dominant force in the world of sentiment analysis, but a rules-based approach is also occasionally implemented.

When rules are employed for sentiment analysis, words are singled out and assessed – usually without much context. The rules themselves are normally lexicons filled with words that have been rigorously labeled by a human being based on the sentiment they tend to express.

A rules-based system is often used to handle niche industries where jargon and acronyms can complicate a more general AI approach.

Turning to rules instead of machine learning also makes sense for smaller teams with limited resources to sink into both designing their own learning model and supplying it with enough labeled data to make it functionally accurate.

Less dynamic applications that demand interpretation of data bound by relatively strict practices are perfect uses for a rules-based sentiment analysis system. However, as specific rules become less clear to those creating them and more prone to changing, an AI-enhanced approach begins to make more sense.

Sentiment Analysis Using AI

Using AI or machine learning to assess human sentiment in text or speech is a popular approach used at present. The accuracy and utility of this approach is determined by both the exact learning method employed and the data used to instruct the machine on how to interpret future input.

Among a growing variety of machine learning methods are Naïve Bayes, K-nearest method, Random Forest and Support Vector Machine. Each of these has its own strengths and weaknesses, balancing speed with accuracy to varying degrees.

Novel varieties have continued to surface, with some even turning to Genetic Programming to help improve sorting accuracy.

All of the above share the same strength of being fit to help researchers predict outcomes and highlight correlations even when those same researchers do not know how to do so manually.

Hybrid Sentiment Analysis

Using either rules or machine learning to assess sentiment can work quite well in many cases, but some applications call for a combination of the two to be employed. There are multiple ways to go about successfully implementing such a hybrid approach:

• Using machine learning to create new rules – This option leverages the dynamic adaptability of AI to assess niche data and generate logically sound rules. Rules created via this method are as accurate as the algorithm employed and can, therefore, outperform human rule-designers in many cases.
• Using rules as feature inputs in a machine learning model – Machine learning models parse features (unique variables) to sort information by predefined criteria. Typically, raw data is fed into an algorithm’s features and interpreted, but information can also be transformed before being used in such a way. By populating an algorithm’s features with the output data from one or more rules, more complex assessment problems can be solved.
• Using rules and AI as feature inputs – Yet another hybrid approach to sentiment analysis uses both rule outputs and AI outputs to define machine learning features for eventual assessment. This approach provides even greater flexibility for solving complex problems, but its complexity can border on excessive with the addition of more models that must be trained and maintained.

Sentiment vs. Emotion

Sentiment and emotion are not considered one and the same. In fact, the latter is far more complex than the former in practice.

Emotions center on the individual who experiences them, arising from a subjective experience while engendering both a physical and behavioral response. Sentiment, on the other hand, can be described as resulting from relationships with other parts of society at large.

Sentiment points outward (opinion) and emotion points inward (mood).

The nature of sentiment limits the results its analysis can produce. Sentiment analysis tends to yield binary results at its most basic level, with results often classed as either positive sentiment or negative sentiment – sometimes a neutral classification is used as well.

This simple polarity provides enough insight when used in a narrow context to make it worthwhile. However, exploring the deeper emotional context behind sentiment can produce more powerful results.

What Part Does Emotion Play in Sentiment Analysis?

Emotion motivates people to develop and express their sentiments, but it does not tie into most sentiment analysis workflows as these are designed to rate data at a higher, less detailed level.

According to Forrester, emotion has a bigger impact on brand loyalty and customer retention compared to other metrics that have long been relied on, such as CX effectiveness and customer engagement.

Measuring emotion has many advantages, such as producing more actionable insights and deepening your understanding of customers’ motivations for interacting with or leaving your company.

Emotion goes much deeper than sentiment, allowing your business to make more relevant and data-driven changes to its operations with emotion-derived insights than with those obtained through sentiment analysis.

For instance, where sentiment analysis would reveal a comment regarding your brand on social media to be positive or negative, emotional analysis would strive to determine how the poster was actually feeling when they mentioned your brand in the first place.

This key difference makes the addition of emotional context particularly appealing to businesses looking to create more positive customer experiences across touchpoints.

Evaluating sentiment along with behavioral metrics arms companies with the insights needed to identify drivers of customer satisfaction and loyalty and take the appropriate action in the moment to create exceptional customer experiences.

In the contact centre, sentiment and emotion analysis helps agents understand how callers are feeling and respond appropriately to make a positive impact and improve customer satisfaction.

Emotion analysis is also crucial for identifying vulnerable customers and gaining insights on the most effective ways to handle vulnerable customers to achieve successful outcomes.

This blog post has been re-published by kind permission of CallMiner – View the Original Article

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