AI-driven Sentiment Analysis- Benefits, Use cases and Implementation
Updated on Nov 25th, 2022
A household name since the 2019 pandemic, Zoom is now garnering headlines again, this time for its new project around sentiment analysis. The company is investing in an emotion-analyzing system that will evaluate the engagement depth and emotions of the users during their online calls. This is not the first time a corporate is investing in this futuristic AI application. Companies like Apple, KFC, Twitter, and TripAdvisor have been using sentiment analysis applications to improve their customer experience and then eventually their services and products for increased revenues.
Thanks to the long-term investments by such conglomerates, AI-driven sentiment analysis has already become a market worth USD 3.15 billion in 2021 and is forecasted to grow at a CAGR of 14.4% by 2030. In this post, we will uncover how different industries are using the technology, what benefits they expect from its application, and how a business can implement the same to future-proof their customer experience.
- AI-driven sentiment analysis (SA) is a market worth USD 3.15 billion.
- The market is predicted to grow at a CAGR of 14.4% till 2030.
- The polarity system is the most basic form of sentiment analysis.
- SA is an effective tool for customer retention and reducing employee attrition.
- InMoment and Clarabridge are pre-built NLP APIs to process SA algorithms.
Table of Contents
What is Sentiment Analysis?
Sentiment analysis is one of AI’s more advanced applications that effectively detect human emotions from texts, audio clips, and even video extracts. The technology uses predetermined metrics to identify positive, neutral, and negative texts and sounds.
To do that, it first analyzes millions of texts and sounds already tagged with a specific emotion. This system is called polarity since it only recognizes the three most basic human emotions and returns results along a single dimension, ranging from +1 or absolutely positive to -1 extremely negative.
The polarity system, while not effective in recognizing the exact emotion of a person, its normalization helps in getting rid of particularities through which people express themselves. That way, the application can help organizations draw meaningful insights and resolve customer experience issues without having to deal with redundant details.
More advanced AI-based sentiment analysis technologies or algorithms use Natural Language Processing or NLP and Machine Learning (ML) models to capture more subtle nuances of human language.
For example, these algorithms are advanced enough to understand that the term ‘sick’ can be used positively in a sentence like ‘Loved Angela’s entire look! The make-up, hair, and outfit are sick!’.
The accuracy of such specific and more subtle emotional reading depends on the accuracy of training and actual data and how similar they are to each other. Also, the more data you feed to the machine learning model, the smarter it will become, using even the real data to keep training itself.
Benefits of AI-based Sentiment Analysis Applications?
Sentiment analysis is a great AI application helping businesses improve their customers’ experience effortlessly. Here we have enlisted some of the other benefits the technology can bring to the table for you.
Improve customer retention by offering them unprecedented personalization in support services. The algorithm will help your post-sales and support team adapt their services as per customers’ moods.
Reduced Employee Attrition
The algorithm is not great just for retaining customers but for valuable employees as well. The tool can be embedded in your staff’s communication software and alert the HR department when it recognizes employee dissatisfaction. The same application can also improve employees’ productivity through customized incentives.
Marketing Strategy Optimization
You can also enhance your marketing efforts by implementing sentiment evaluation against your competitors’ marketing tactics. That way, you can easily ignore the campaigns that are unlikely to bring positive results, focusing more on the promising ones.
Enhanced Product Engineering
Improvement in product engineering is yet another benefit companies that engage in production can experience due to this technology. They can mass evaluate the product’s new features before even launching the same and then decide whether or not to invest time and resources in it.
Developing AI solutions such as a sentiment analyzer can help you identify happier customers enabling you to improve the conversion rates for your upselling efforts. It can also recognize customers with heavier spending capacities to help your team narrow down prospects.
Real-time Emotion Detection
Sometimes, agents have to handle more than one customer at a time. As such, it can be challenging to see how each customer feels at a given time. This is where the custom-developed AI-based sentiment analysis program can help.
Multiple Customer Handling
Unlike the regular live chatbots that only work on FAQs, a more advanced one with capabilities to understand human emotions can help you resolve more pressing customer issues. The team will be able to know at a glance which customers need more attention and then quickly switch from the regular support drill to a more customized one.
Eliminate Emotional Triggers
You can also identify and avoid strong emotional triggers by evaluating the common phrases your support, sales, and marketing teams use. The algorithm will help you identify those phrases that continually annoy or aggravate customers and leads, and then you can replace them with better terms.
With these numerous promising benefits, many companies are openly investing in technology. Now, we’ll learn how they are doing so as per their specific industry workflows, operations, and requirements.
Use Cases for the Sentiment Analysis Applications
With integration into CRMs and social media channels, the technology is helping businesses across industries. Here we will discuss some of the more important use cases businesses from various industries use.
Retail businesses can use technology to identify trends and gauge how consumers feel about their brand. Deloitte has been working on the use cases for the largest retail franchises in South Africa.
The company analyzed customers’ sentiments over social media channels, figuring out that most negative emotions around the retailers are about the turnaround time of their support services. The analysis also helped the chains understand the upcoming trends like innovative technologies like NFC and solutions supporting sustainability. Both are huge investments, and the retailers hesitated to take the step until the sentiment analysis algorithm clarified all their doubts.
Similarly, a clothing retail giant has been using the technology to derive in-depth semantic insights from social media websites and user-generated videos to recognize trends early on. That has given the company a competitive edge, and a more comprehensive look into customer behavior segregated based on geography, language, age, gender, etc.
Tourism and Hospitality
In travel and hospitality, the tool is mostly used to evaluate guest reviews to understand what aspects of the business are most valuable to the guests and which cause them the most issues. This way, they can leverage their stronger aspects to market their products and services to a similar audience set. On the other hand, they can also prioritize fixing the problems that most customers face and express negative feelings about.
Other than that, the travel and hospitality businesses can also ascertain what services/amenities are doing well with the guests and which are not. That way, they can invest more resources in the ones eliciting positive responses from guests and skip the ones that bring little difference to their experience but cost the business money.
The companies already benefiting from adopting the above-mentioned use cases are Travel Media Group, Sentiment140, etc. Now considering how the pandemic-induced conditions have affected the industry in recent years, adopting such avant-garde solutions can help them gain and retain customer loyalty, the real currency in travel & hospitality.
Sentiment analysis is quickly becoming increasingly popular in the healthcare industry thanks to its ability to measure the effectiveness of different accreditations, medical staff training, and the effects of new measures on patients’ comfort.
Most applications in healthcare use sentiment analysis to detect problems and strengths just like a business in any sector would; what’s different here is the complexity involved.
Words used in clinical narratives are quite different than in the other sector. Even the social media clinical conversations are different than the actual conversations on the field, the latter consisting mostly of nouns and body locations. That can lead to a less subjective use of language in the field, making sentiment analysis more niche and complex.
If there’s any sector that can benefit the most from sentiment analysis technologies, it has to be telecommunications. After all, the entire industry’s success depends on how well they handle consumers’ demands, complaints, and experience requirements. One popular example from the industry comes from a big European mobile network operator.
The company has been recording its support calls for quality analysis and converting the same into a text form. Later it would run those texts through its advanced machine learning algorithms to find out the customers who are most distressed and were negatively affected by the last call they had with the company’s customer representative.
After identifying such individuals, the company would send them apologies in text messages with custom discounts. As a result, they have now automated customer retention mechanisms and have improved their churn rates.
Banking and Finance
With the help of AI-powered sentiment analysis, finance institutes, including banks, can improve their customer acquisition and retention strategies by investing in sentiment analysis technologies. They can gauge how customers are responding to the offers being run by their competitors on social media.
If the customers show positive sentiments on those campaigns, they can also consider framing something similar for their brand. The organizations can also learn about the customers’ major pain points with surgical precision.
In a sentiment analytics study conducted by a banking institution, they were able to pinpoint the major concern faced by most customers, which is the unavailability of support services during their free time, mostly during lunch hours. That helped the company invest its resources in the right direction, improving brand positioning and customer retention rates.
Implementation of a Sentiment Analysis Program
The main goal of Sentiment Analysis is to obtain the emotion from the context. That could be data from online reviews or investors’ feelings about new changes. Now, manually processing all this can delay the analysis workflow, making it impossible for companies to make timely adoptions. That’s why implementing a sentiment analysis program with an end-to-end integration is necessary. Here’s how you can do that through a sophisticated sentiment analysis software development approach-
Step 1: Data Collection
Before you develop sentiment analysis software, it’s important to create a model for it. The first step to building a model from scratch is to collect relevant data. For example, suppose you want to collect data from social networks like Facebook and Twitter to analyze how prospective clients, customers, and other stakeholders are showing their sentiments about your brand. In that case, you can collect all that data from the respective APIs.
Other sources you can collect data from to analyze the emotion of people at large about your business, its competitors, or certain campaigns even, including review websites, CRMs, sales responses, feedback, etc. Generally, data thus collected can be categorized into three kinds: structured, semi-structured, and unstructured. It is, therefore, also important to categorize and organize the data separately to ensure easy processing.
Step 2: Training Dataset and Subjective Data
Once you’ve collected all the necessary data, it’s time to create and train the model on it. While doing so, please note that you will need two types of datasets to develop sentiment analysis software. One for subjective information and the other for unbiased information.
The subjective dataset will include the notion inside the setting, while the impartial dataset will be free of any sentiment or emotion. The former conveys the emotion in two ways: the extracted data shows positive or negative emotions. Once the model gets trained on enough data of both kinds, it can be used as a classifier for test and real-time data.
Step 3: Data Cleaning
With the model prepared, you are all set to analyze real data. For that, your data engineer will create an ETL or Extraction, Transformation, and Loading pipeline. But for that to work, you’ll have to perform adequate data cleaning. This process is done to ensure that data is semantically compatible before the model works on examining the vocabulary.
For example, on a review platform, individuals may post content outside the grammatically correct English language. They may even use exclamation marks or slang to emphasize their points. As such, this data should be pre-processed or cleaned to get categorized into a neat semi-structured format. The cleaned data will be free of any deviations that may lead to ambiguous sentiment analysis.
Step 4: Real Time Analysis
The cleaned data, organized semantically in semi-structured and structured formats, can now be analyzed in real time. For this part, the AI developer you hire can consider using pre-built APIs like InMoment or Clarabridge, that is, if your requirements match their outputs. Otherwise, they will develop a text analysis platform to extract sentiments from emails, reviews, text documents, messages, news articles, and social media posts.
They can create a custom solution to map the frequently used phrases into categories for quicker analysis. After fine-tuning, those terms can be associated with a more specific emotion. Other than quicker analysis, if your organization caters to a target audience that speaks in jargon or subject-specific terms, a custom solution built by a sentiment analysis software development company will be your best fit.
How Can Matellio Help?
Matellio is a pioneer in developing cutting-edge AI solutions. With our years of experience creating custom solutions for various industries, we have unparalleled expertise in designing ETL pipelines and data processing. As such, our flawlessly engineered AI-based sentiment analysis software can assist you in building and scaling your business on nothing but positive emotions and brand image.
Our team of highly skilled AI developers understands the end-to-end processing of AI sentiment analysis solutions for a variety of industries and businesses. They understand not only the data evaluation and modeling part of the process but also the technical development. From UI development and ML model development to API integration and cloud configuration for seamless integration, they can take care of every part of the development process without needing any external help.
To know more, you can take a look at our work in AI/ML solutions or, better even, book a free consultation call with our experts. They will create the project timeline and tech-stack report and send the free quote for you to decide, with no strings attached.
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