Companies need to understand their audience if they want to improve their services, business model, and customer loyalty. However, having a dedicated team monitoring social networks, review platforms, and content-sharing platforms is inefficient. A wiser solution would be to implement sentiment analysis in NLP (natural language processing) to analyze customer feedback automatically.
Many businesses are using these technologies to their advantage. In fact, the market for NLP solutions is expected to reach $43 billion in 2025 (from only $3 billion in 2017). In addition, the sentiment analysis software market may reach $4.3 billion by 2027 (starting from $1.6 billion in 2020).
So if you’re eager to discover why sentiment analysis and other NLP approaches are getting common for businesses, keep reading. You’ll also learn how to overcome the typical challenges companies face while implementing them. But let’s start with the basics.
What is sentiment analysis in natural language processing?
Natural language processing (NLP) allows computer programs to read, decipher, and understand human language from unstructured text and spoken words.
This is made possible with data preprocessing and data processing. Data preprocessing means transforming textual data into a machine-readable format and highlighting features for the algorithm. Data processing is a rule-based system built on linguistics and machine learning systems that learn to extract meaning from information.
This may sound complicated, but we often experience NLP in daily life. All the speech-to-text tools, chatbots, optical character recognition software, and digital assistants (like Alexa or Siri) you like so much are powered by NLP.
But speaking is so much more than uttering words. People tend to put lots of emotions into their speech, the emotions computers have trouble “understanding.” That’s when sentiment analysis comes into play.
Sentiment analysis is a subfield of NLP that explores the meaning, emotional tone, and personal information surrounding words. Sentiment text analysis helps you identify:
- The emotional background behind the text (positive, negative, neutral, or mixed)
- Detect specific feelings rather than the overall tone (like shock, anger, or sadness)
- Identify the intent behind the text (the user can write an entire review just to express frustration about the inability to pay with PayPal)
- Determine connections (a positive review for a product might include bad sentiments about the shipping service)
In other words, if your software can collect and identify words with NLP, sentiment analysis gives these words emotion and context. Now, how can you apply this in your business?
How businesses apply sentiment analysis and NLP
Sentiment analysis and NLP might not seem that valuable business-wise. However, they can provide exceptional value for customer-focused companies. Let’s take a look at the most common applications of sentiment analysis across industries.
Sentiment analysis software can analyze feedback about your marketing campaigns on social networks, review platforms, and forums. It helps you understand your ads’ implications on the target audience, allowing you to personalize or rethink your approach.
Expedia Canaday used sentimental analysis to detect an overwhelmingly negative reaction to the screeching violin music in the background of its ad. The company then produced a follow-up ad with the actor from the original video smashing the violin. This helped abandon an unsuccessful campaign early on and show that the company is in touch with its audience.
Software that combine users’ personal data and sentiment assessment can identify attitudes towards specific products. For example, ad networks and e-commerce platforms can target users with products similar to those they praised on Twitter or remove ads for those they hated.
ANAGOG helps companies target customers with personalized ads and campaigns. Unicsoft helped the client enhance this platform with sentiment analysis, NLP, and machine learning algorithms that allow targeting customers with products based on their feedback on the web.
Sentiment analysis, in conjunction with reputation management software, can process all channels where people discuss your brand (websites, corporate emails, support tickets, and chat logs). Evaluating customers’ reactions at scale can help you identify business areas that need improvement.
Additionally, you can set up a notification about negative comments on the web. This lets you immediately direct your agents to communicate with discontent customers. As a result, you mitigate bad reviews and show your attachment to every customer.
Businesses tend to research their competitors based on what their customers say about them online. This gives you a good idea about the strengths and weaknesses of other industry players. Based on that knowledge, you can reevaluate your priorities, adjust your business model, and craft tailored messages to promote your benefits over the competition.
Your competitors can be direct and indirect, and it’s not always obvious who they are. However, sentiment analysis with NLP tools can analyze trending topics for selected categories of products, services, or other keywords. It’ll help you discover other brands competing with you for the same target audience. Plus, it gives you a glimpse into the qualities people value most for specific products.
Employee sentiment monitoring
Do you want to make sure you have a friendly working environment? Then, standard methods like annual performance reviews, turnover rates, and anonymous surveys won’t be enough.
Sentiment analysis and machine learning allow companies to analyze information on websites, corporate emails and chats, and even videos on platforms like YouTube. It’ll give you a better understanding of the things your employees like (or dislike), how they communicate within the office environment, and how engaged they are throughout the day. Your HR team can later use this data to address specific issues or change company policies.
Social engineering can persuade employees to share sensitive corporate information or download malware, which may lead to a data breach. According to the 2020 Phishing Benchmark Global Report, even in organizations that undergo security training, 20% of staff still click on phishing emails.
Text classification and sentiment analysis tools can detect email and messaging applications phishing. They scan language with signs of social engineering, like overly emotional appeals, threatening language, or inappropriate urgency. NLP software also filters email scams based on the overuse of financial terms, misspelled company names, and other characteristic spam-related words.
Supply chain management
Customer sentiment plays a key role in the efficiency of supply chain networks. Based on the 2022 MHI Annual Industry Report, the biggest challenge for supply chain disruptions for 51% of businesses is customer demand.
Many analytics platforms have NLP tools to monitor customer sentiment and geopolitical implications across countries. Together with other data, it helps them forecast chain disruptions and demand changes. It’s also established that context-aware sentiment analysis can potentially improve the efficiency of logistics companies and supply chain networks.
And, of course, NLP helps you improve your customer support. You’ll be able to measure people’s reactions when talking with your support agents, making it easier to rank their effectiveness. It’ll also help you identify the most recurring topics and concerns of your customers.
Plus, you can automate redundant tasks. For instance, solutions like Watson Natural Language Understanding can identify keywords, categorize documents, and summarize support tickets. It also automatically classifies incoming support messages by topic, polarity, and urgency.
To top it off, sentiment analysis tools can enhance your chatbots by allowing them to correctly interpret the emotional background of messages and respond in an appropriate tone. Digital agents like Google Assistant and Siri use NLP to have more human-like interactions with users.
That was quite a list! However, adopting sentiment analysis and other subtasks of NLP isn’t as straightforward as you might think.
Challenges of implementing sentiment analysis and NLP
NLP isn’t easy to implement properly. Take a look at the most common challenges you might face and ways to solve them.
Analysis can be inaccurate
NLP isn’t yet capable of interpreting human language with 100% accuracy. For example, most software has trouble detecting the true meaning of negative sentiments (like: “Thanks for taking only 2 weeks to reply to my email!”). The same goes for sarcasm and irony, which are also often misunderstood by real people.
Sentiment analysis software can misidentify emotions in comments written in a neutral tone. For example, a customer submitting a comment “My smartphone casing is blue.” could be identified as neutral. But, in reality, the customer ordered a red case and is actually complaining about the wrong color.
However, machine learning can train your analytics software to recognize these nuances in examples of irony and negative sentiments. Some systems are trained to detect sarcasm using emojis as a substitute for voice intonation and body language. And this brings us to the next problem, which is data dependency.
Dependency on data
To provide accurate results, NLP requires large data sets. That’s especially hard for smaller companies and startups, who’ll need months to collect enough data for their platforms.
Training your algorithms might include processing terabytes of human language samples in documents, audio, and video content. In that case, you’ll benefit from a scalable cloud computing platform and efficient tools for filtering low-quality data and duplicate samples.
Alternatively, you can use pre-existing models that were trained on data sets. Off-the-shelf solutions like Google Natural Language API offer a collection of NLP models already tuned by Google. This would help you make informed decisions without spending months on test data.
Reliance on other solutions
Sentiment analysis alone isn’t enough for effective customer feedback research. You might need to enhance your platform with other NLP subtasks, such as:
- Syntax analysis for understanding the syntactic structure of sentences, morphology, grammatical mood, and tense
- Named entity recognition to detect products, brands, public figures, locations, as well as the emotional reactions they invoke
- Text classification and content clustering to automatically categorize and group analyzed data
- Speech recognition to extract textual information from videos and audio content
- Visual detection that measures the sentiment based on facial expression
- Semantic search to help e-commerce websites understand customer’s intent or predict products they’re more likely to buy
Depending on your requirements, this list may include other NLP and AI-powered approaches.
Technical knowledge and experience
Preparing training data, deploying machine learning models, and incorporating sentiment analysis requires technical expertise. Not only that, but you also need to understand which NLP solutions are feasible for your business.
And don’t overlook cybersecurity, either. Your team should ensure compliance with security and privacy laws to avoid legal problems (which is especially important for healthcare and FinTech software).
That’s why sentiment analysis and NLP projects need experienced engineers, data scientists, security specialists, and managers. But if you don’t have professionals like that on board, a reliable software development company can help you bridge those gaps.
How we implement sentiment analysis into your business
At Unicsoft, we have over 15 years of experience in software development, IT consulting, and team augmentation services. Our approach is tailored for every client, but here’s how we can take over your project.
- Discovery. We’ll discuss your problems, goals, and vision for your project. Then, our experts will analyze your business and the existing IT architecture to find the NLP solutions that would benefit your company and its customers the most. Finally, we create a product roadmap that considers your requirements, deadlines, and budget.
- Data preparation. Our team ensures you have enough high-quality data for accurate analysis. We provide data mining services, including existing mentions of your brand and relevant training data. Afterward, we transform the data into a machine-readable format, fix grammatical mistakes, and exclude irrelevant information.
- Coding and integration. Our engineers will create a minimum viable product (MVP) to help you validate your project faster. Then, we’ll integrate the sentiment analysis and NLP functionality into your systems according to your requirements.
- Support and maintenance. We’ll continue supporting your product long after deployment. You can focus on your business while we maintain your application, roll out software and security updates, and monitor your system’s performance.
We developed a robust customer feedback analytics system for an e-commerce merchant in Central Europe. The system collects customer data from social networks, aligns their reviews with given scores, and analyzes their sentiment. Just one year after deployment, our system helped the client improve its customer loyalty program and define the marketing strategy, resulting in over 10% revenue improvement.
Unicsoft offers a wide variety of NLP solutions for startups, SMBs, and enterprises. And you’re welcome to learn about other successful projects in our portfolio here.
Sentiment analysis in NLP is extremely valuable for customer-oriented businesses. It can help you research the market and competitors, enhance customer support, maintain brand reputation, improve supply chain management, and even prevent fraud.
But NLP is challenging to implement, as you need an advanced technical stack, machine learning algorithms, and high-quality test data. Besides, you need a thorough strategy to understand how to enhance your business capabilities.
That’s where Unicsoft can assist you. By choosing our company, you get a reliable partner, personal dedication, and over a decade of experience. We can implement sentiment analysis, NLP, and other AI technologies into your platform or develop your solution from scratch. Just contact us if you want to learn about our services.