Nowadays, customers aren't just buying products, they're buying experiences.
All e-commerce businesses are sitting on a treasure trove of valuable customer feedback. The problem is that most of them don't know how to extract meaningful insights from it.
According to a 2021 survey by NewVantage, only 39% of executives believe their organizations are delivering on their data strategy.
But AI & machine learning have made it possible to analyze sentiments and interpret customer feedback quickly and accurately.
Sentiment analysis, also less commonly called opinion mining, analyzes and interprets large volumes of customer feedback to identify and understand customer emotions, opinions, and attitudes toward a particular product or service.
So, how does customer sentiment analysis work? Let's take a closer look.
How Does Customer Sentiment Analysis Work?
Traditionally, the customer sentiment analysis algorithm used to utilize computational linguistics and natural language processing (NLP) techniques to extract public sentiments from unstructured data such as customer reviews, social media posts, and survey responses.
Tracking customer sentiment manually is nearly impossible due to the sheer volume of data generated daily. Natural Language Understanding (NLU) automatizes this process by teaching machines to extract interactions from touchpoints and classify them into positive, negative, or neutral sentiments.
The consumer sentiment definition determines whether feedback is positive, negative, or neutral.
Here are the steps in a nutshell:
Data collection
The first step is to gather customer feedback from various sources, including social media, review sites, and customer surveys.
Data preprocessing
The collected data is then cleaned and organized to remove irrelevant information and prepare it for analysis.
Sentiment detection
Using NLU techniques, the data is analyzed to measure customer sentiments expressed in the text.
Sentiment classification
The sentiments are classified into positive, negative, or neutral categories.
Sentiment analysis score
The customer sentiment score is then assigned based on the number and intensity of positive or negative sentiments expressed.
Visualization
Finally, the results are presented in an easy-to-understand format, such as graphs or charts, for further analysis and decision-making.
Introduction of LLM to Sentiment Analysis: The Modern Approach
Large language models (LLMs) like GPT-4 and BERT have shifted the paradigm of natural language processing techniques traditionally used since 2023 and improved sentiment analysis by accurately detecting nuanced emotions and contexts that traditional methods might miss. These models benefit from techniques such as domain-specific fine-tuning, transfer learning, and data augmentation, which significantly improve their accuracy. LLMs have extensive applications in customer service, brand monitoring, market research, political strategy, and financial forecasting, demonstrating their wide-ranging utility.
For instance, if you're a business owner launching a new product and want to understand your audience's perception or a policymaker trying to gauge public opinion on a recent decision, the vast amount of data from social media and other platforms can be overwhelming. LLMs process this data efficiently, providing a more nuanced and context-aware analysis than traditional NLP methods.
By training on large datasets, these models can grasp language subtleties, detect sarcasm, and identify mixed feelings or sentiment shifts within a single document, understanding context, tone, and even humor. This advanced capability enables businesses and policymakers to extract actionable insights from raw data, improving decision-making and strategic planning.
Advanced sentiment detection
LLMs allow for a more sophisticated detection and classification of sentiments, moving beyond basic positive, negative, or neutral categories. For example, they can contextualize sentiment analysis based on industry-specific jargon and adapt dynamically to evolving linguistic trends. This makes them exceptionally valuable for businesses that require cutting-edge sentiment tracking across various communication channels. Their scalability and continuous learning from incremental data inputs help refine the algorithms, ensuring that sentiment analysis models remain robust and relevant over time
This integration not only improved the precision of customer feedback analysis but also enriched the insights businesses can gather from social media posts, customer reviews, and other digital interactions. Processing and analyzing this data in real time allows for timely and informed decision-making.
Why the difference matters?
Large Language Models (LLMs) utilize transformer architectures, which are highly effective at processing sequential data and capturing long-range dependencies in text.
These models apply self-attention mechanisms to weigh the importance of each word in relation to others, enhancing the ability to discern nuanced sentiments. Additionally, LLMs are trained using techniques such as unsupervised learning on extensive corpora, enabling them to develop a sophisticated understanding of language nuances. This deep contextual awareness allows for precise sentiment detection and categorization across varied text inputs, making them invaluable for real-time sentiment analysis in dynamic market conditions.
What is a Customer Sentiment Analysis Score?
A numerical value representing customers' overall sentiment towards a product or service is known as a customer sentiment analysis score. Typically, it ranges from -1 (extremely negative) to +1 (extremely positive), however, in practice, it can be represented within any range.
The sentiment analysis score provides a snapshot of customer attitudes and opinions. For example, if the score is above 0, customers have a positive sentiment towards the product or service. While a score below 0 suggests negative sentiments.
The score can also be broken down into specific categories like product features, customer service, or delivery experience to identify improvement areas.
What are the Types of Sentiment Analysis?
Customer sentiment analysis's desired outcomes and objectives shape the approach and techniques used. Here are 9 common types of customer sentiment analysis models:
Polarity-based
A NLP technique that categorizes text into positive, negative, or neutral sentiments based on the overall sentiment expressed in the language. Polarity is numerically represented as a sentiment score ranging from -100 (extremely negative) to +100 (extremely positive) and 1 for neutral sentiments.
Social media sentiment analysis of text uses polarity-based analysis to track and measure the overall sentiment of a brand or product.
Aspect-based
Aspect-based sentiment analysis focuses on specific aspects or features of a product or service. When you read a restaurant review, and the customer raves about the food but complains about the service, that's an example of aspect-based sentiment analysis.
Any customer feedback that mentions a specific product review, such as price, color, size, or usability, is an example of sentiment analysis focused on particular aspects.
Fine-grained
As the name suggests, fine-grained sentiment analysis provides a more detailed understanding of customer sentiments by breaking them into smaller categories.
For instance, a hotel review might mention the rooms' cleanliness, service quality, and amenities separately. Fine-grained text analysis captures these nuances to provide a more comprehensive view of customer opinions.
Emotion-based
Emotion-based sentiment data goes beyond identifying positive, negative, or neutral sentiments and categorizes them into happy, sad, angry, or surprised emotions.
The analysis considers the tone and intensity of the language to determine the underlying emotion. For instance, a review that says, "I am extremely disappointed with my purchase," would be classified as negative customer sentiment with an angry emotion.
Opinion mining
Opinion mining, also known as subjectivity analysis or sentiment summarization, focuses on identifying subjective information in customer feedback rather than objective facts.
For example, "The food was delicious" expresses a positive opinion, while "The food was cold" implies a negative opinion.
Intent analysis
Intent analysis, also known as opinion detection or sentiment classification, identifies the intention behind a customer's statement.
For instance, if a customer says, "I love this phone because of its long battery life," it reflects a positive sentiment with an intent to highlight the product's features. On the other hand, if a customer says, "I hate this phone because it constantly freezes," it indicates a negative sentiment with an intent to warn others about the product's flaw.
Entity-based
This brand sentiment tracking focuses on identifying the entities or objects mentioned in customer feedback and analyzing their sentiments. These entities can be products, brands, services, or specific features.
For example, a laptop review mentioning its brand name and processor speed would fall under entity-based sentiment analysis.
Temporal
Temporal sentiment analysis considers the time factor and how sentiments change over time. It tracks the sentiment towards a product or service at different times to identify trends and patterns.
For instance, the sentiment towards a new product may be positive during its launch but decrease over time as customers use it.
Multimodal
Multimodal sentiment analysis combines different modalities, such as text, images, and videos, to understand customer sentiments better. Video content, for example, may require a combination of audio and visual analysis to determine the sentiment expressed accurately.
What are the Benefits of Customer Sentiment Analysis?
A customer sentiment analysis provides valuable insights into customer opinions and preferences. Here are some of the key advantages of conducting a customer sentiment analysis:
Understanding customer preferences
The companies can use sentiment analysis to identify the features, aspects, or services customers value the most. Traditional surveys and feedback forms may not provide a comprehensive view of customer preferences. The CSAT score, for instance, may not capture the nuances of customer opinions.
In contrast, sentiment analysis can gather and analyze large volumes of customer feedback to determine their preferences accurately.
Customer satisfaction scores are a popular metric for analyzing customer experience and informing decision-making. 80% of companies use it to improve customer experience.
Identifying areas for improvement
Sentiment analysis identifies common pain points and areas where customers are dissatisfied. Companies can prioritize and address customer issues by analyzing negative feedback to improve customer satisfaction.
83% of customers say companies should respond to complaints and feedback quickly. Companies must monitor and react to negative sentiments to reduce churn rates and increase customer loyalty.
Improve the customer experience
Your product might be fantastic, but your service could ruin the customer experience. Sentiment analysis can help identify trends and patterns in customer interactions, enabling companies to make data-driven decisions.
For instance, a sentiment analysis of online reviews may reveal that customers are unhappy with the delivery process. The customer service team can use this information to improve their delivery services for a better customer journey.
Informing product development
By analyzing customer feedback, companies can gain insights into what customers like and dislike about their products or services. The development team uses this information to refine existing products or develop new ones that better meet customer needs and preferences.
Customer sentiments can also provide valuable input into the product roadmap and help prioritize features or services based on customer demand.
Monitoring brand reputation
With the increasing use of social media platforms, customers have a powerful platform to share their opinions and experiences with brands. Sentiment analysis helps companies monitor their brand's online reputation and quickly address any negative sentiment before it escalates.
For instance, a negative viral tweet about a product flaw can damage a company's reputation if not addressed promptly. Customer sentiment analytics help companies identify and respond to such issues efficiently.
Competitor analysis
The more you understand your customers' preferences and sentiments, the better you can position yourself against competitors. Analyzing customer feedback can reveal your company's strengths and weaknesses compared to competitors.
For example, if customers consistently praise a competitor's product feature that your product lacks, it may be an area for improvement or development. With sentiment analysis, you can stay informed about your competitors' reputations and customer sentiment to inform your strategy.
Optimizing marketing campaigns
You invest significant resources in marketing campaigns, and sentiment analysis will help you understand how customers respond.
Some of the questions sentiment analysis can answer are where to spend a marketing budget, which campaigns are most effective, and how to make your messaging more appealing to your audience.
Sentiment analysis allows us to identify negative feedback or backlash towards a particular campaign—a chance for you to adjust or redirect your marketing efforts before it's too late.
When is it Worth to Build Your Customer Sentiment Analysis Software?
The situations where building your customer sentiment analysis software may be worth considering are:
Unique requirements
Off-the-shelf software for sentiment analysis may not always meet the specific needs of your business. For example, you may want to analyze customer sentiments in different languages or dialects unsupported by existing tools.
Your industry or niche may require specialized sentiment analysis techniques to capture customer opinions accurately. In such cases, building your software may be the best option. The software can be customized to your unique requirements and provide more precise analysis.
Domain-specific analysis
Some industries, such as healthcare or finance, have specific regulations and terminology that require a more specialized sentiment analysis approach. In these cases, generic customer sentiment analysis tools may not provide accurate results.
For instance, in healthcare, sentiment analysis needs to consider the sensitivity of patient data and adhere to HIPAA regulations. Similarly, sentiment analysis may need to factor in legal jargon or complex financial terminology in finance.
Building your software allows for a more tailored and accurate sentiment analysis based on your industry's unique needs.
Integration with existing systems
If your company already has a robust data infrastructure, building your sentiment analysis tool that integrates with existing systems can be beneficial. Collecting, storing, and analyzing customer feedback can be automated, saving time and resources.
Additionally, integrating with existing systems allows you to combine sentiment analysis with other data sources, such as sales or marketing data, for a more comprehensive understanding of customer sentiment.
Data privacy and security
Customer data is sensitive; companies must protect it from unauthorized access or breaches.
Building your sentiment analysis software gives you greater control over data privacy and security measures. You can safeguard customer data by implementing encryption protocols, access controls, and regular security updates.
Track Your Customer’s Sentiments with the help of iRonin.IT
At iRonin.IT, we can provide custom software development services to help companies build powerful sentiment analysis tools. With our data science and machine learning knowledge, we can create a custom sentiment analysis solution that meets your unique business needs.
We have experience working with all verticals, from e-commerce to healthcare, and provide domain-specific sentiment analysis solutions. Our team also ensures data privacy and security by implementing robust encryption protocols and complying with industry regulations.
