What is Sentiment Analysis? The Definitive Guide for 2024

Our deep-dive into sentiment analysis: turn your customer reviews into powerful insights to propel your business forward in 2024.
Written by:
George Swetlitz
Published on:
June 12, 2024
Our analysis of over 100000 restaurant review

Table of contents

About The Author

George Swetlitz
-
Co founder
George is a co-founder of RightResponse AI and a former CEO of a healthcare clinic group with 200+ locations. His experience using customer reviews to drive organic growth and internal improvement led to the creation of RightResponse AI.

Introduction to Sentiment Analysis

You might be wondering: “what is sentiment analysis?” In reputation and review management, sentiment analysis is the process of computationally identifying and categorizing customer feedback to determine a customer’s experience towards a particular aspect of your business, product, or service. At a high level, sentiment analysis gauges whether the experience the customer is writing about is positive or negative.

Analyzing unstructured data (like customer reviews) has traditionally been a very time-intensive process. But how can you use that data as a business owner or manager to improve your products and services? Are there more efficient ways to analyze customer feedback rather than sifting through those manually?

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With the advancement of AI technologies, NLP (natural language processing) and LLMs (large language models), many companies are automating this process at scale to efficiently turn customer feedback into actionable insights that propel your business and your brand’s reputation forward. 

What does sentiment analysis do?

Sentiment analysis enables you to better understand your customers' opinions by applying metrics to text-based feedback. It allows you to gauge how your business is performing from your customers' perspective. These metrics can then be visualized and categorized, pinpointing the strengths and weaknesses of your overall brand while diving deeper into particular aspects of your products and services. 

Within review management, this customer review sentiment analysis is extremely useful for multi-location businesses that receive numerous reviews and want to understand how their business locations compare based solely on customer feedback.

Why is Sentiment Analysis Important?

Sentiment analysis is crucial for enhancing the customer experience by providing businesses with a deep understanding of their customers’ feelings and opinions. By analyzing customer feedback, businesses can quickly identify both positive and negative sentiments, allowing them to address issues early and improve their products or services. This proactive approach not only enhances customer satisfaction but also fosters loyalty, driving long-term business success. Furthermore, by resolving issues and improving operations, businesses can increase the likelihood of receiving better ratings and reviews, which positively impacts their online presence.

In addition to improving customer experience, sentiment analysis plays a vital role in guiding business strategies. It offers actionable insights that help businesses understand market trends and customer preferences. By leveraging these insights, companies can tailor their marketing campaigns, product development, and customer service efforts to better meet customer needs. This data-driven approach ensures that business strategies are aligned with customer expectations, leading to more effective and targeted initiatives. As a result, higher ratings and positive reviews can enhance a business’s position on Google Maps, making it more visible to potential customers.

Monitoring brand health is another significant benefit of sentiment analysis. By continuously analyzing customer sentiment across various touchpoints, businesses can keep a pulse on their brand’s reputation. This ongoing monitoring helps identify potential risks and opportunities, enabling businesses to maintain a positive brand image and stay competitive in the market. Improved ratings and reviews not only boost search engine rankings but also create a better first impression for anyone reading about the business. Overall, sentiment analysis is a powerful tool that supports informed decision-making and strategic planning, ultimately contributing to a stronger, more resilient brand.

Types of Sentiment Analysis

Sentiment analysis can be broadly categorized into these types

  1. Polarity detection - Classifying text as positive, negative, or neutral. 
  2. Emotion detection - Identifying emotions such as joy, anger, or sadness.
  3. Intent analysis - Discerning the user's intent behind a message or phrase.
  4. Aspect-Based Sentiment Analysis - Tying the positive, negative, or neutral classification to a particular aspect or topic important to the person doing the sentiment analysis..

Each type of sentiment analysis offers unique insights best suited for different applications..

Polarity Detection

Polarity detection is one of the most common types of sentiment analysis. Strings of text and phrases are analyzed and classified as positive, negative, or neutral. In some cases, polarity precision could be identified as important and implemented by grading sentiments on a scale of very positive to very negative (think of star ratings, for example). 

Emotion Detection

Emotion detection sentiment analysis allows for identifying specific emotions being expressed in feedback. However, this can be particularly tricky to implement since people express emotions in many different ways, and it’s possible to express completely different emotions using similar language.

Intent Analysis

Intent analysis tries to uncover the underlying intention behind a user’s message. Are they seeking information? Are they in the process of making a purchasing decision? Intent analysis can help businesses and marketers prioritize their marketing campaigns by quickly identifying keywords and phrases that will likely drive revenue as soon as possible.

Aspect-Based Sentiment Analysis (ABSA)

Aspect-based sentiment analysis (ABSA) goes deeper by evaluating text sentiment toward specific aspects, products, services, or business features. This precision makes it much easier for businesses, operations managers, and reputation researchers to pinpoint specific areas for improvement quickly.

This is the style of sentiment analysis employed by RightResponse AI. 

Sentiment Analysis Example

Let's take an inside look at how our AI-powered review sentiment analysis software breaks reviews down into positive and negative sentiments by categories and topics.

Review Analyzed:

"Wife and I are from Texas and have been looking for juicy fajitas like back in Houston. They were very juicy and tender here, but they lost me by drowning them in lime juice. Totally lost the flavor of the meat. I was also put off by not being able to order a single enchilada a la carte. The rice had no flavor at all. That said, the service was fantastic. Very friendly and attentive. The vibe was amazing! The food was well prepared and hot for what it is - but just not my palette." - Bianca P. ★★★★

After being analyzed by sentiment, you can view the individual phrases that were identified right within the app and see how those phrases were categorized into more specific topics along with the strength of the sentiments (in parenthesis).

Customer review sentiment breakdown

You can see from the above image, from one single review, 10 phrases were identified as containing sentiment and those sentiments were positive for 5 topics and categories and negative for 2 topics.

You'll also notice each topic identified with sentiment is given a strength in parenthesis next to the topic (i.e. Taste (2)).

Now, instead of only extracting one singular data point (the star rating from a review) like traditional review platforms, our AI-powered sentiment analysis engine creates several, even dozens of data points in some cases, from each text review.

To extract and visualize this data in a way that is helpful, we've created a metric called percent positive mentions (PPM). The percent positive mentions metric is tied to each category and topic that is setup in the settings dashboard. These topics and categories are initially created from templates based on your primary business category in Google, but these can be modified at any time to your liking.

Sentiment analysis data by categories and topics with % Positive Mentions.

RightResponse AI's Sentiment Analysis: Aspect-Based with Machine Learning

Our easy-to-use AI engine harnesses the power of aspect-based sentiment analysis (ABSA) enhanced with machine learning scalability. This sophisticated approach was leveraged by a former CEO of a healthcare business with 200+ locations to help understand the customer experience, allowing organizations to identify specific improvement opportunities, increasing retention and new customer acquisition.

“We were getting what added up to hundreds of reviews every single day… We didn’t have the tools to dig out the nuggets that were lying just under the surface, especially at scale.”

With RightResponse AI, Google reviews can be thoroughly analyzed for sentiment, enabling companies to extract critical insights to improve the customer experience and build a more positive reputation.

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How can you use sentiment analysis to improve customer experience?

Sentiment analysis performed by our platform breaks down each text-based review into phrases, which are then assigned to categories and topics relevant to that business, along with the strength of those sentiments. These insights uncover how customers feel about very particular aspects of the business.

For example, a multi-location restaurant franchise could visualize positive and negative sentiments expressed over Categories like Food Taste/Quality, Pricing, Atmosphere, Customer Service, and even compare performance across locations. 

ABSA in RightResponse AI breaks down unstructured feedback into structured, actionable insights, focusing on specific aspects of a product or service. This method not only identifies what customers feel but also why they feel that way, providing a detailed map of customer satisfaction and areas for improvement.

Customization and Precision

RightResponse AI’s platform is designed with customization at its core, allowing businesses to tailor the analysis to their specific needs. You can add or remove Categories and Topics to focus on the aspects most relevant to your business. Multiple Topics can be grouped within a Category, and you can customize these groupings as needed.

RightResponse AI also allows for “Context” for the positive and negative aspects of Topics.

For example, what does Accessibility mean for your business? Does it refer to the convenience of the location within the town, or does it pertain to accessibility for people with disabilities? Defining the context helps the LLM (large language model) understand exactly what you mean, ensuring that the analysis aligns with your business perspective.

This level of customization enhances precision. At RightResponse AI, we further define precision by assessing the strength of sentiment. We categorize sentiment strength on a scale: 3 indicates very strong (either positive or negative), 1 indicates weak (around neutral), and 2 encompasses everything in between.

Our approach to precision involves breaking down each review into phrases, assigning those phrases to specific Topics, assessing their sentiment strength, and then reporting the positive and negative sentiments. This multi-step process ensures that the insights generated are both relevant and actionable, helping you make informed decisions based on precise customer feedback analysis.

Bridging the Gap with ABSA

RightResponse AI's use of ABSA with machine learning is a bridge between the unstructured customer feedback businesses receive every day and the structured, actionable insights businesses need to thrive. It's not just about understanding if the sentiment is positive or negative but understanding the why behind it, at scale.

Sentiment Analysis Use Cases

From analyzing customer reviews to gauge product satisfaction to monitoring social media for brand sentiment, sentiment analysis finds application across industries. These real-world examples highlight its versatility and impact on business intelligence.

Optimizing Operations for Small Businesses

For small businesses, sentiment analysis can be a game-changer in understanding and improving customer experience. By analyzing customer reviews, small business owners can pinpoint specific areas where their products or services excel or need improvement. This immediate feedback allows them to make swift adjustments, enhancing overall customer satisfaction. Small businesses can leverage these insights to refine their offerings, leading to increased customer loyalty and positive word-of-mouth.

Additionally, better understanding customer sentiments can lead to higher ratings and reviews, which are crucial for small businesses competing in a crowded market. Improved reviews boost the business’s visibility on platforms like Google Maps, making it easier for potential customers to find them. A stronger online presence and positive first impressions can significantly impact the growth and sustainability of small businesses, helping them to attract and retain more customers.

Managing Reputation for Multi-Location Businesses

Multi-location businesses face the unique challenge of maintaining consistent service quality across different sites. Sentiment analysis helps these businesses monitor and compare customer feedback from various locations, identifying patterns and discrepancies in customer experience. This information enables management to implement targeted improvements where needed, ensuring a uniform standard of service and enhancing overall brand reputation.

By systematically addressing issues highlighted in customer reviews, multi-location businesses can improve their ratings across all locations. Higher ratings contribute to better rankings on Google Maps, making each location more visible to potential customers. This visibility not only drives foot traffic but also ensures that potential customers have a positive perception of the brand before even stepping through the door.

Informing Marketing Strategies for Brands

Marketing departments can greatly benefit from sentiment analysis by gaining deep insights into customer perceptions and preferences. By analyzing customer reviews, marketers can identify which aspects of their products or services resonate most with customers and which need improvement. This information allows them to craft more effective marketing campaigns that highlight strengths and address weaknesses, ensuring that their messaging aligns with customer expectations.

Sentiment analysis can help brands monitor the effectiveness of their marketing efforts in real-time. By tracking changes in customer sentiment following a campaign, brands can quickly gauge its impact and make necessary adjustments. Positive shifts in sentiment can lead to higher ratings and reviews, which enhance the brand’s online presence and attract new customers. This proactive approach to marketing not only boosts brand reputation but also drives business growth.

Enhancing Online Visibility for SEO Agencies and Consulting Firms

SEO agencies and consulting firms can use sentiment analysis to provide their clients with valuable insights into their online reputation. By analyzing customer reviews, these firms can identify patterns and trends that affect their clients’ search engine rankings. Understanding the sentiments behind customer feedback helps in crafting strategies that address negative reviews and capitalize on positive ones, thereby improving overall online visibility.

Higher ratings and positive reviews play a crucial role in boosting a business’s ranking on search engines and platforms like Google Maps. SEO agencies can use sentiment analysis to help clients achieve better rankings, making it easier for potential customers to find them. Consulting firms can also use these insights to provide actionable recommendations that enhance their clients’ customer experience and reputation, ultimately leading to more successful and competitive businesses.

Integrating RightResponse AI: A Unique Approach to Sentiment Analysis

RightResponse AI harnesses the power of advanced machine learning models to deliver sophisticated sentiment analysis tailored to the unique needs of businesses of all sizes. Our platform’s extensive customization options allow businesses to define specific Categories and Topics, adding or removing them as necessary to focus on the most relevant aspects of their operations. This level of customization ensures that the insights generated are both precise and actionable, addressing the exact concerns and interests of the business.

In addition to customization, RightResponse AI provides a comprehensive contextual understanding of sentiments, allowing businesses to define what specific Topics mean to them. For instance, the term “Accessibility” can be contextualized to mean either the convenience of a location or its suitability for people with disabilities. This contextual clarity ensures that our large language model accurately interprets customer feedback in alignment with the business’s perspective.

Furthermore, our platform breaks down reviews into phrases, assigns these phrases to defined Topics, assesses their sentiment strength, and reports the positive and negative sentiments. This meticulous approach provides businesses with the detailed insights they need to enhance customer experience, guide strategic decisions, and maintain a competitive edge in the market.

Getting Started with RightResponse AI

Implementing RightResponse AI in your business is straightforward. Interested businesses can begin with a 14-day free trial, experiencing firsthand the benefits of advanced sentiment analysis.

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*No credit card required.

Sentiment analysis has become an important tool for gaining deep insights into customer opinions and emotions. By effectively understanding and leveraging these insights, businesses can enhance the customer experience, refine their strategies, and build a competitive edge. RightResponse AI leads the way in this technology, providing a comprehensive and innovative solution for any business that wants to make sense of their unstructured customer review data.

Related FAQs

What is sentiment?

Sentiment can be defined as an attitude, thought, or judgment prompted by feeling. Within text-based reviews, multiple sentiments can be identified toward particular topics, categories, or services and then analyzed at scale to extract helpful data that can be used to improve your products or services and receive more positive reviews in the future.

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