Quick Summary
Sentiments are central to shopping behaviors and they guide buying decisions. Organizations have realised that they can identify consumer sentiments, attitudes, and emotions towards their brand by utilizing sentiment analysis tools and technologies for opinion mining, text analysis, and brand perception analysis. Sentiment analysis services can help you know the type of sentiment analysis you need to undertake and the right approach, data sources, and tools needed for such analysis.
Introduction
Have you ever thought how many businesses know what customers think of them or their brand? It is not that they hire mentalists to read customers’ minds. They deploy a technique known as sentiment analysis. As companies cannot read minds, but they deploy AI-powered sentiment analysis tools to read and analyze a large volume of texts (tweets, emails, reviews, feedback, and customer notes) to gauge the sentiments of customers towards a brand.
AI sentiment analysis tools have features and competencies to extract and analyze user-generated content on various channels, and then based on it, decide whether the sentiment expressed is positive, negative, or neutral. Knowing the key types of sentiment analysis and the right one for your business can help you gain from the benefits offered by the sentiment analysis approach.
In this blog, X-Byte—as a leading sentiment analysis service company, shortlists the 12+ types of sentiment analysis that can be used by organizations to understand their customers better, along with a detailed analysis of which type is best suited for a particular secto
What is Sentiment Analysis?
Gartner defines Sentiment Analysis as “the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information”. It is used by organizations to gauge what’s hidden behind the texts, feedback, speech, tweets, social media posts, product choices, and comments of the customer.
Almost 80% of customers believe that the experience a company provides is as important as its products or services. A survey conducted in France, Germany, the United Kingdom, and the United States in late 2021, highlighted that 52% believed a friendly and helpful approach to be an enabler of great customer experience.
However, gauging customer sentiment is as complex as understanding human nature.
Old-school ways for sentiment analysis, like surveys, feedback forms, group chats, and one-on-one talks are ineffective to gauge the real feel of what customers think as they often rely on direct answers. Direct survey methods can be biased and don’t derive insights in real time.
This is when AI-powered natural language processing-based, aspect-focused sentiment analysis, and other techniques became key to understand customers’ comments across different touchpoints
AI Sentiment Analysis: Going Beyond Text and Detecting Implied Meaning
Technologies like NLP sentiment analysis using deep learning algorithms powered by artificial intelligence and machine learning are used for creating ruled-based sentiment analysis models.
In simple words, the sentiment analysis tool is trained to recognize and classify lexicons (certain words, keywords, group of words, phrases, and text blocks) that define a particular sentiment. For instance, a customer review about an ordered dress says–‘poor fabric’, the sentiment analysis tool can recognize the word ‘poor’ and gauge the sentiment as negative.
AI-powered sentiment analysis is one step ahead of basic NLP based textual sentiment analysis. It can also detect sarcastic tones, idioms, slangs, or negations in a text.
For instance, a customer reviews an earphone– “Extremely wonderful. They were so well-made that they lasted one full week.” The mere rule-based sentiment analysis will catch the words like ‘wonderful’ or ‘well-made’ and term this as a positive sentiment. However, AI-based sentiment analysis can detect the sarcasm by identifying incongruence between words and implied meaning.
Top 12+ Types of Sentiment Analysis Apt For Your Business
Getting accurate insights from customer feedback, reviews, and other sources is a tough task, and sentiment analysis has proven to be effective in understanding customer sentiments and improving engagement, for businesses in the fields of retail, healthcare, finance, customer service, etc, as it helps identify pain points, predict customer behavior, and personalize interactions.
Here are the best 12+ types of sentiment analysis that can be used by businesses:
1. Fine-Grained Sentiment Analysis
Traditional sentiment analysis tools failed to find context-specific emotions and granular insights within customer feedback and fine-grained also called aspect-based sentiment analysis can help businesses tackle these by delivering precise sentiment categorization, deeper contextual understanding, and targeted action plans. Using opinion mining they extract detailed opinions about the sentiments on quality, price, or service.
Best for: E-commerce, hospitality, product reviews, and customer service.
Why: These sectors have tried using traditional Sentiment Analysis Methodologies but failed to identify context-specific emotions and granular insights. With fine-grained sentiment analysis categorizing sentiments precisely at the aspect level becomes easier and faster. This is especially true for industries where businesses need to pinpoint customer sentiment about specific product features, service aspects, or brand interactions.
For instance, an e-commerce company can determine whether a customer is satisfied with product quality but unhappy with delivery service. This allows for targeted improvements.
2. Emotion Detection Analysis
Customers express their love or displeasure about a product or service through words. A happy customer is most likely to use words like happy, frustrated, and excellent service, while opposites of these may signify unsatisfactory experiences. Emotions detection sentiment analysis tools along with emotional analytics help organizations understand the major sentiments of customers through techniques like TF-IDF, RAKE NER, or deep learning-based models. PepsiCo’s Chief Strategy and Transformation Officer Kanioura has highlighted that AI-based analysis has helped
PepsiCo to understand what consumers want and how they want to be engaged. They have used data sets to deliver products and create experiences that are highly customized.
Best for: Retail, healthcare, finance, and customer service
Why: Gauging emotions is much more than detecting positive, negative, and neutral sentiments. It helps identify deeper emotions of the customer like joy, anger, frustration, and satisfaction. For the sensitive healthcare sector, this understanding helps address patient problems, for finance, it helps predict market trends, while in retail it helps develop personalized marketing campaigns.
Take control of your brand’s narrative with our advanced sentiment analysis services!
3. Social Media Listening Sentiment Analysis
Traditionally analysts spent around 80% of their time gathering information before starting the actual work of analyzing it. Social media sentiment analysis and cloud-based sentiment analysis have changed this. Most customers are very vocal on social media platforms, especially the aggrieved ones. They share their opinions on social media about brands that have failed in their services. Therefore, social media listening or social media sentiment analysis can help businesses identify customer sentiments, slander campaigns, brand perceptions, and potential crises.
This is particularly helpful in consumer-driven industries like retail, hospitality, finance, and entertainment, where public sentiment directly impacts business success. For instance, fashion brands have over time started using social media analysis to gauge public reaction to new collections and adjust their marketing strategies accordingly.
Best for: Consumer-driven industries like retail, hospitality, finance, and entertainment
Why: Twitter, Instagram, and Facebook are hubs of social media platforms where customers go no-hold-barred and are ready to appreciate, criticize or condemn the product. Social media sentiment analysis has been highly beneficial for businesses to track brand reputation, detect emerging trends, identify influencers, and respond to potential PR crises in real-time.
4. Real-time Sentiment Analysi
In certain cases like luxury retail, healthcare, financial services, and hospitality, delivering custom products or services to customers can go a long way in building customer loyalty and retention. Personalized experiences powered by AI-powered real-time sentiment analysis, used in tandem with cloud-based sentiment analysis for decision-making, help in automated intent recognition, proactive issue resolution, and hyper-personalized recommendations. Luxury retail brands are known to use real-time analysis to offer hyper-personalized shopping experiences based mainly on customer feedback.
Best for: Luxury retail, healthcare, financial services, and hospitality
Why: Real-time sentiment analysis influences how businesses connect with customers and react right away to their needs. Spotting unhappy customers can be a great way to adjust strategies, and companies can fix problems before they get worse, boosting customer loyalty and keeping more customers.
5. Text-based Sentiment Analysis
Text sentiment analyzers have proven to be extremely beneficial for organizations as they decipher customer emotions, identify sentiment trends, and detect emerging issues. And enhances decision-making. Data sentiment analysis helps in turning all the unstructured data generated by enterprises like emails, chat transcripts, social media comments, reviews, support tickets, etc., into insights that enable businesses to improve customer experience, optimize operations, and drive strategic growth.
Leading text platform Twitter is a classic case where text sentiment analysis uses text analytics and does so by classifying positive tweets from negative tweets using machine learning sentiment detection. These help in classification data analysis and data visualization, helping organizations know how customers think about their brand and what conversations are happening around them.
Best for: Customer support, telecom, and online platforms
Why: The amount of text data generated from emails, chat transcripts, support tickets, and reviews is overwhelming, and businesses can turn this big data into something worth using for business benefit by using text sentiment analysis. It helps organizations analyze this data, improve customer experience, strategize operations, and drive decisions.
6.Generative AI Sentiment Analysis
Generative AI has disrupted traditional approaches to customer experience management, and Gen AI sentiment analysis can help organizations gain a deep insight into the exact reasons, type, and number of customer complaints. It empowers organizations by combining structured and unstructured data with advanced analytics capabilities to identify quality issues, and complaints arising out of those issues. Gen-AI sentiment analysis has brought almost a 50% reduction in production defects.
Best for: Manufacturing, quality control, and product development
Why: While structured data is comparatively tangible and insights can be drawn from it, unstructured data lying in systems across the enterprise may seem to be just data without any use. AI-driven sentiment analysis combines structured and unstructured data and helps organizations gain incredible insight, and detect quality issues and customer complaints early. This can also help to reduce production defects and improve product quality. 7. Natural Language Processing (NLP) Sentiment Analysis
7. Natural Language Processing (NLP) Sentiment Analysis
Some companies fail to recognize the potential of speech and hence are unable to get benefits from speech analytics. Thus just basic insights are derived from the customer’s speech and the direction to be followed in terms of usage of this speech. Using NLP sentiment analysis can help organizations in identifying avenues for increasing sales, or improving customer satisfaction. Telecom companies use NLP to analyze call center interactions and improve customer service strategies.
Best for: Call centers, sales, and customer experience teams
Why: Customer calls have been considered to be a means of solving or addressing the concerns of the customer. However, every call might not end positively and organizations lack transparency into the overall sentiment of the customer. Speech-based customer sentiment analysis has shown to help businesses get useful insights from customer calls. On top of that, NLP sentiment analysis lets businesses spot potential sales chances and key areas to improve customer satisfaction.
8. Aspect-based Sentiment Analysis
Sometimes, the subtleties of a customer’s sentiment cannot be gauged just by basic analysis; it needs a deeper contextual understanding to capture the nuances of positive, negative, or neutral emotions. It involves analyzing text at a granular level, considering context, tone, and underlying emotions to accurately classify sentiments as positive, negative, neutral, or even mixed.
Best for: E-commerce, hospitality, product reviews, and customer service
Why: Context-specific emotions and granular insights weren’t a big aspect of the last decade. However, with aspect-based customer sentiment analysis organizations can focus on identifying and analyzing sentiments towards specific aspects or entities within a text. It helps businesses categorize sentiments more precisely at the aspect level.
9. Intent-Based Sentiment Analysis
The Intent-based sentiment analysis services work on the principle of identifying the underlying motivation behind customer feedback, reviews, or interactions. It usually combines customer sentiment tracking which involves the identification of positive, negative, or neutral sentiment along with intent analysis where understanding the purpose or goal is the end goal.
Best for: Chatbots, virtual assistants, and automated customer service.
Why: Understanding the intent and the sentiment can create magic for businesses and help them deliver accurate answers to customer queries or provide customers with excellent service. Intent-based analysis uses the dual combination of sentiment and customer intent, to improve customer interactions by understanding not just how a customer feels but also what they want.
AI-driven chatbots are excellent examples of intent-based sentiment analysis services that differentiate between a complaint and a general inquiry and deliver an accurate response.
10. Brand Perception Analysis
Building a brand takes more than just a great product or service, it requires strategic adaptation and continuous engagement. After the launch of products and services, the brand owners need to understand the pulse of the audience and make strategies that help them achieve their business goals while delivering quality. Brand perception analysis helps them do exactly that by employing social media sentiment analysis, customer feedback mining, and AI-powered analytics for tracking brand reputation, identifying customer sentiment trends, and making informed decisions to improve customer experience and loyalty.
Best for: Marketing and branding teams, PR agencies
Why: Maintaining a strong market presence takes more than just offering quality products or services; it requires continuous brand monitoring, strategic adaptation, and proactive customer engagement. Brand perception analysis helps organizations do exactly that by monitoring public opinion, tracking sentiment shifts, and making informed marketing decisions. Companies launching new products use this method to gauge audience response and refine marketing campaigns.
11. Feedback Sentiment Analysis
Most often brands fall short in getting feedback from their customers and those that are received are often scattered, vague, or lack actionable insights. By using feedback sentiment analysis, brands can categorize and interpret customer emotions, identify recurring pain points, and uncover hidden opportunities for improvement. Leading SaaS companies are known to use feedback sentiment analysis to enhance product features based on customer reviews.
Best for: Product development, UX teams, and service-based businesses
Why: Customer feedback is often scattered and vague. Feedback sentiment analysis categorizes responses, identifies trends, and helps uncover opportunities where business improvements can be made.
12. Hybrid Sentiment Analysis
Hybrid sentiment analysis involves the use of lexicon-based and machine-learning sentiment detection approaches to leverage the strengths of both, improving accuracy and adaptability in customer sentiment analysis tasks. Financial institutions are known to use AI sentiment classification and hybrid analysis to track investor sentiment in stock markets and gain benefits from it.
Best for: Some businesses like finance, healthcare, and legal industries require accuracy in sentiment detection, and getting the sentiments right is a high priority.
Why: A potent combination of lexicon-based and machine-learning approaches can help these actors thrive by improving accuracy and adaptability, making hybrid sentiment analysis ideal for industries with complex language and jargon.
Detect, analyze, and respond to customer sentiments in real-time across multiple channels.
13. Comparative Sentiment Analysis
Today’s discerning consumers make a purchase decision by comparing multiple products or services. Comparative sentiment analysis businesses understand how their offerings stack up against competitors, to identify their strengths, weaknesses, and unique selling points, refining their marketing and product strategies accordingly. This is done by analyzing customer feedback, reviews, and discussions. This method enables brands in Industries like e-commerce, automotive, and consumer electronics to benefit greatly from this approach.
Best for: E-commerce, automotive, and consumer electronics.
Why: Companies can understand their brand positioning by using comparative sentiment analysis for analyzing customer reviews and social media discussions. They can check what works for them and what needs to be improved. This is particularly useful in price-sensitive and feature-driven markets like e-commerce, cars, and gadgets.
14. Industry-Specific Sentiment Analysis
Every industry is different the response or feedback of customers varies based on those industry-specific parameters. Industry-specific sentiment analysis is based on custom AI models that detect relevant emotions and trends within a particular field, ensuring more accurate and actionable insights.
Best for: Healthcare, finance, and hospitality
Why: Hospitals and clinics need not only to provide value-based care but also to see if what they are delivering is translated into a happy patient. Industry-specific sentiment analysis helps understand the feedback for service improvements, while in finance, it tracks investor sentiment and market trends, aiding in risk assessment and strategic decision-making.
The hospitality sector can monitor guest satisfaction and improve customer experiences, thus delivering more precise insights by customizing AI models to industry-specific language and concerns.
Choosing the Right Sentiment Analysis Methodologies
Though each of the above-mentioned sentiment analysis tools can prove to be extremely beneficial, organizations need to incorporate a particular type based on their specific use case, business goals, and based on what they want to achieve.
In scenarios like brand perception tracking, feedback analysis, or emotion detection in customer service, solely using brand perception analysis or emotion detection sentiment analysis would suffice, as these methods directly address the core objective. However, in more complex cases like the launch of a product, crisis management, or competitor benchmarking, a combination of multiple sentiment analysis types may be required.
Hence it is necessary for companies to strategically select and combine sentiment analysis methods, to get maximized insights, refine strategies, and make smart decisions for long-term success.
Wrapping Up
At X-Byte Analytics, we deliver sentiment analysis services that combine social sentiment analytics with real-time sentiment analysis to help leading organizations across retail, e-commerce, healthcare, automotive, etc, make data-driven decisions faster.
Harness the power of high-end sentiment analysis tools to track brand perception, decode customer emotions, and refine your strategies.
Whether it’s real-time monitoring or in-depth analytics, X-Byte Analytics has got you covered!
Stay Ahead with Smarter Sentiment Analysis!
