What is Sentiment Analysis: Concepts, Use Cases & Applications [GUIDE]

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Enterprises are struggling to get value out of their data to understand the sentiments of their customers. What are the customers speaking about them? How are their products and services performing in the market? These questions are crucial to answer. Sentiment analysis techniques can bring out valuable insights from customer feedback spread across social media, forums, review websites, and more.

In this article, we’ll highlight the importance of sentiment analysis in enterprises and how machine learning-driven applications can help them improve their customer experience.

What is Sentiment Analysis?

Sentiment analysis uses Natural Language Processing (NLP) to understand whether the opinions mined are positive, negative, or neutral. Companies run Sentiment analysis over texts such as customer feedback on brands and products to understand their views.

Types of Sentiment Analysis

Sentiment analysis helps to determine the polarity of sentiments such as positive, negative, or neutral. It’s also valuable for mining and analyzing emotions such as anger, happiness, sadness, etc.

Sentiment analysis is used to understand the importance or urgency of a task and to gauge the interest of a group or individual in something. Data scientists and experts can design sentiment analysis as per their requirements.

Here are some popular types of sentiment analysis:

Fine-grained Sentiment Analysis

Using this type of sentiment analysis one can figure out how polarised emotions are. For example, the opinion poll before an election shows where the sentiments of the people lie. This type of analysis provides a more profound understanding by expanding the polarity categories, such as:

1. Strongly agree
2. Agree
3. Neutral
4. Disagree
5. Strongly disagree

This analysis is similar to a 5-point scale where “Strongly agree” is a 5-star and “strongly disagree” is a 1-star.

Emotion Detection

Sentiment analysis is designed to mine and understand emotions. Lexicons are commonly used for this purpose. However, there is a drawback in using dictionaries. The same set of words can have opposite meanings based on context and culture.

Thus, sentiment analysis with Machine Learning algorithms tends to be more helpful. Emotion Analytics is increasingly using emoticons that reflect unique facial expressions for specific emotions.

Intent Analysis

Sentiment analysis can be designed to understand whether the customer or prospect is interested in a particular product or not. It can also help businesses understand whether a customer intends to make a purchase.

Aspect-based Sentiment Analysis

This is a technique that helps businesses conduct in-depth research of qualitative customer feedback. For example, when customers talk about the shelf-life of a product, the longevity of a battery, the user experience at a POS, or the response time for an online or telephonic query, their tone can be positive, negative, or neutral.

Complex Machine Learning algorithms can help determine the underlying sentiment in such statements. For example, if a customer says, “…the response time for making online queries is too long,” the algorithm can identify a negative sentiment here.

At Gramener, we’ve developed an Aspect-based Sentiment Analysis model that helps organizations understand customer sentiments about a product’s quality.

Multi-lingual Sentiment Analysis

Multi-lingual sentiment analysis is more difficult and complex because it involves more than one language. It requires the data scientists to develop specific codes for the resources. It also involves a lot of pre-processing and resources for the Machine Learning Programs. Text classifiers can facilitate the process by helping to detect the language in the text, and pre-trained programs can convert the text into a language of choice.

How to Do Sentiment Analysis: Exploring Various Methods

Wondering how sentiment analysis works? There are several methods to gauge emotions in texts, each with its unique approach. Let’s break it down simply:

• Rule-Based Systems: Sentiment analysis can employ predefined rules to determine sentiment based on specific words or phrases. Positive words indicate positive sentiment and negative words indicate the opposite. While straightforward, it might lack nuance.
• Machine Learning Algorithms: Machine learning models, like Support Vector Machines or Naive Bayes, learn from labeled data to predict sentiment. They can handle complexities and nuances, making them widely used in sentiment analysis tasks.
• Deep Learning Models: Deep learning techniques, such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks, process sequential data and can capture intricate patterns, enhancing the accuracy of sentiment analysis.
• Hybrid Models: Combining rule-based systems, machine learning algorithms, and deep learning models can create hybrid approaches. These models leverage the strengths of each method, offering a more comprehensive analysis.
• ChatGPT for Sentiment Analysis: ChatGPT, powered by advanced natural language processing (NLP), is a revolutionary way to analyze sentiment. By conversing with the model and examining its responses, you can gauge the underlying sentiment. Its contextual understanding and vast knowledge make it adept at interpreting emotions in text conversations. Plus, it’s user-friendly and doesn’t require extensive technical knowledge. Check out our latest blog on ChatGPT for Sentiment Analysis and learn how the Large Language Models (LLMs) are augmenting customer experience.

Remember, the choice of method depends on the specific use case and the complexity of the sentiment analysis task at hand. Whether you opt for traditional rule-based systems, sophisticated machine learning algorithms, deep learning models, or the simplicity of ChatGPT, each method offers a unique pathway into the fascinating world of sentiment analysis.

Benefits of Sentiment Analysis

In an environment where customers offer candid feedback, data and consumer opinion are beneficial. Companies that can glean sentiments from customer surveys or social media conversations can apply those insights to product and service designs. Here are some more benefits:

Sorting Large Quantities of Data

The internet generates an exponential amount of data. Sorting the same manually is an impossible task. Machines trained to sort large quantities of data can reveal important insights about human sentiment at a fraction of the same time and cost.

Real-time Analysis

Human sentiments are fleeting. Pre-trained Machine Learning-based programs can identify such emotions in real-time from social media conversations. For example, suppose the actions of a party-worker have enraged the public during the election season. In that case, senior party members can quickly identify the public sentiment and take requisite corrective action to prevent churn in the election.

Improving Customer Experience

• Adaptive Customer Service: With the help of sentiment analysis, companies can gain insights into the customer mindset and understand their issues in real-time. This allows them to adapt as per their customer needs and innovate.
• Reduces Customer Churn: One of the biggest reasons companies lose customers is a lack of behavioral understanding. Customer sentiment analysis conducted regularly through consumer feedback and surveys is one of the best ways to reduce customer churn and improve service.
• Tracking Overall Customer Satisfaction: Tracking the online footprint of your customers and gauging their pulse can help you solve their problems promptly. Sentiment analysis can help companies improve their customer satisfaction scores.
• Devise Better Marketing Strategies for Customer Experience: The better the consumer understanding, the stronger the foundations of your marketing strategies. Insights from sentiment analysis can be a springboard for new and innovative marketing strategies.

Gauge Public Opinions in Real-time from Social Channels

Whether it’s the launch of a new product or an election campaign, it is possible to assess the public reaction on social media instantly. This allows marketers to change or adjust their campaigns or respond to the public in real-time. Social media sentiment analysis can play a vital role to understand the presence and popularity of a product or person in the competitive market.

Example of Sentiment Analysis: A real-time case study from Gramener

One of our leading computer technology service provider clients wanted to better understand the Voice of their Customers (VoC). They sought to analyze almost over 1 million comments generated annually on its computer hardware and related services.

To synthesize the unstructured data in these comments, the global giant wanted a solution that involved text mining. This would provide actionable insights that would help the company make informed business decisions. Using this futuristic solution, the organization aimed to overcome the low buyer demand and economic slowdown.

Gramener’s NPS Analytics Solution uses Machine Learning and Deep Learning to extract Sentiment Polarity and the Moment of Truths (MoTs) from chats, comments, speech, and other forms of written text. The following machine learning algorithm was used to determine the level of customer satisfaction and improve the NPS score of the client.

This entity extraction has a very high accuracy of 90-98 percent. It enables the client to serve the customers better by focusing on the right problem areas. After implementing this solution the client’s Net Promoter Score (NPS) increased by 7 points. It also led to year-on-year growth in revenue of about USD 10 billion (8-9 percent).

Role of AI in Sentiment Analysis

Sentiment Analysis uses Machine Learning (ML) and Natural Language Processing (NLP) algorithms to synthesize data and reveal emotional undertones. Reference dictionaries give positive or negative scores to different words. An average score for all words of the text is computed to provide an idea of how positive or derogatory a given text is. Following are the different algorithms that can be used to achieve sentiment analysis:

• Supervised Machine Learning: In this approach, using Machine Learning techniques and various features to build classifiers that can identify emotions behind the text. The training data must be of good quality for superior results with this method. For example, a politician releases a speech. This speech would then go through an NLP in sentiment analysis program, identifying some popular names mentioned by the politician. Then the text around those names would be analyzed for polarity.

• Rule-based methods: This is an automatic sentiment analysis system with pre-crafted rules. Using reference dictionaries to judge the sentiment of the text. For sentences loaded with sarcasm, negations, or other dependent clauses, manually devised rules are used to reach accurate sentiment scores.
  
• Hybrid: This method combines rule-based, and Machine Learning approaches to provide accurate results.

Sentiment Analysis Use Cases and Applications

Everyone everywhere has an opinion today. More importantly, everyone has a platform to voice their opinion. Thus, sentiment analysis finds use across industries and segments. From consumer durables to FMCG, technology, retail, finance, and even politics, every sector can benefit from sentiment analysis. Here are some important uses:

Social Media Monitoring

Businesses can suffer big time if customer issues spiral out of control. By monitoring social media, businesses can take timely corrective action and prevent adversities. One recent example is the United Airlines fiasco, where a passenger was moved out of an overbooked flight.

The co-passengers captured the issue and shared it on social media. The CEO had to issue an apology later. Organizations can avoid such issues if regular sentiment analysis of social media channels is carried out.

Analyzing Voice of Employees

Employee churn has enormous costs, averaging 20-30% of employee salary, as per the Center for American Progress. With a fifth of all employees voluntarily moving out every year and another fifth being let go, companies’ costs are enormous.

HR teams want to minimize these costs by understanding employee issues and reducing attrition. Sentiment analysis on employee surveys, emails, and other messages can provide a rich understanding of the underlying employee sentiment. HR teams can use this information to address the issues on time and prevent churn.

Analyzing Customers’ Voices

Businesses can use Net Promoter Score (NPS) surveys to determine how positive or negative customers are about their businesses. With specific targeted questions like “How likely are you to suggest this brand/product to your friends or family,” companies can find out how many brand champions they may be looking at.

Such VOC analyses can also help businesses understand what percentages of customers are passive or detractors. Based on such analyses, organizations can engage in loyalty programs to improve the level of all customers to “promoters.”

Brand Monitoring and Reputation Management

Perhaps one of the most critical uses of sentiment analysis is brand monitoring and reputation management. Brands have consumer data across social media channels, websites, blogs, customer feedback surveys, etc. By monitoring these sources on a real-time basis, brands can engage with regions or demographics based on the insights they glean.

Product Analysis

Sentiment analyses of product performance from product reviews and customer feedback can help marketers make product enhancements or resolve issues. Sentiment Analysis is beneficial in making changes before the product is launched at scale. Moreover, product sentiment analysis can also shed light on after-sales service and thus help improve customer service standards and stickiness.

Determine Key Promoters and Detractors of the Business

As discussed earlier, it is possible to successfully identify the key promoters and detractors of business by studying the VOC. By analyzing the reasons behind the sentiment, companies can take corrective action to convert detractors into promoters of the brand.

Pros and Cons

Manually reading through survey responses can achieve the same results as sentiment analysis. Fortunately, it extracts actionable insights from customer sentiments within minutes, if not seconds. Sentiment analysis is not a full-proof replacement for manual reading. The nuances of specific comments can be understood only after going through them. Thankfully, sentiment analysis may help in identifying which comments need immediate attention.