Sentiment Analysis Using NLP: The Go-To Guide

Using Sentiment Analysis, businesses can gauge customer sentiment more accurately and win deals, every time! In 2021, businesses have become adept at acquiring customer engagement data. However, an over-reliance on these data points often leads to the same businesses designating customer feedback as a mere metric -- a very one-dimensional way of listening to the voice of the customer. The voice of the customer cannot be badged and translated into just a number. It must be read, distilled, and most importantly, understood. The truth is, brands need to actively listen to what their customers are saying on every channel they engage with them on - be it calls, emails, or live chat. Monitoring and analyzing the sentiment behind customer feedback should be every brand’s priority, but brands have long struggled to process this data and turn it into actionable insight. With Sentiment Analysis, that’s no longer the case. Here is what we’re covering in this article:What Is Sentiment Analysis?

• Benefits Of Sentiment Analysis
• Different Types Of Sentiment Analysis
• How Does Sentiment Analysis Work?
• Applications of AI Sentiment Analysis In Business
• Challenges In Sentiment Analysis
• How Aviso's Sentiment Analysis works

What Is Sentiment Analysis? 

Sentiment analysis--also known as conversation mining-- is a technique that lets you analyze ​​opinions, sentiments, and perceptions. In a business context, Sentiment analysis enables organizations to understand their customers better, earn more revenue, and improve their products and services based on customer feedback. The difference between a software system capable of analyzing customer sentiment and a salesperson/customer support rep trying to deduce it is the former’s sheer ability to derive objective results from raw text -- This is primarily done through natural language processing (NLP), and Machine learning techniques. Now that we’ve broadly defined what Sentiment Analysis (SA) is, and how it can help businesses understand customer pulse and play the prospecting game better, let’s delve deep into how it actually works, the overarching benefits of SA, and the intricacies/challenges behind it.

Also Read: How To Forecast Sales Accurately

Key Benefits Of Sentiment Analysis:

Here are the important benefits of sentiment analysis you can’t overlook.

• Gives your ear-to-the ground user feedback to improve your product
• Enables better prospecting and an increase in sales revenue
• Improves upselling opportunities among your product’s champions
• Enables proactive customer service
• Help understand your brand’s perception with your audience.

Numbers can give you the raw performance of a particular marketing campaign, engagement levels in a prospecting call, and the number of tickets pending from a customer support perspective. But it won’t tell you why a particular scenario happened and the exact reason behind it. For example, analytics tools like Google and Facebook can help you measure the success of your marketing efforts but they don’t give you an in-depth understanding of why that particular campaign was successful. In this aspect, Sentiment Analysis can be a game-changer. Sentiment analysis helps fill these gaps by telling you how your customers perceive your brand/product. This can give you a huge edge in crafting a highly targeted marketing strategy that resonates with your audience, polishing that follow-up pitch with a high-value prospect, or even helping understand customer pain points through the tone of their emails, thereby drastically reducing Mean Time to resolve (MTTR).

Also Read: How To Build A Go To Market Strategy

Types Of Sentiment Analysis

Typically SA models focus on polarity (positive, negative, neutral) as a go-to metric to gauge sentiment. But sentiments can also be tailored to fit specific use-cases and needs of a business, For example, a customer service rep would glean customer satisfaction insights from the tone of an email, or the mood of the customer on a call, whereas a sales rep might need to know the engagement levels of a prospect (engaged or disengaged) from a particular meeting they had, so they can plan their next follow-up and close the deal.

Here are some of the most popular types of Sentiment Analysis methods:

Standard Sentiment Analysis:

This is one of the most common types of Sentiment Analysis since it recognizes the overall tone of a written text and classifies it as positive, negative, or neutral. Some common examples of text and how categorization happens:

• 'I love how Facebook connects people around the world ' → Positive
• “I still need to see if the Facebook messenger is actually useful to me or not' → Neutral
• “Facebook is so confusing to me, it is not intuitive at all' → Negative

Fine-Grained Sentiment Analysis:

This method employs a more elaborate polarity range and can be used if businesses want to get a more precise understanding of customer sentiment/feedback. The response gathered is categorized into the sentiment that ranges from 5-stars to a 1-star.

• Very positive - 5 stars
• Positive - 4 stars
• Neutral - 3 stars
• Negative - 2 stars
• Very negative - 1 star

Aspect-Based Sentiment Analysis:

While fine-grained analysis helps you determine the overall polarity of your customer reviews, aspect-based analysis delves deeper. It helps you determine the particular aspects (attributes or components) of a product people are talking about. Imagine you’re a salesperson selling a project management solution, you had a meeting with a prospect and are convinced that the meeting went well, but running the recording of the meeting through a Sentiment Analysis tool can reveal the actual subtext behind certain statements. Take this phrase for example: “This solution definitely fits within our budget, even though our IT teams could find the API infrastructure a bit tricky to scale. I will be happy to talk to other stakeholders and see if we can move things forward” - even though this might be construed as largely positive by the salesperson, a SA tool can help deduce the negative sentiment associated with the phrase, and the possibility of the deal falling through.

With aspect-based analysis, you can determine that the prospect has said something “negative” about your “API infrastructure scaling well in the long-term”, you can quickly act on this by setting up a meeting with your solution engineers and making sure queries around your product are addressed.

Also Read: Best Revenue Forecasting Models

How Does Sentiment Analysis Work Under The Hood?

There is a myriad of techniques and methods that are used to analyze sentiment, this differs from one organization to another based on their needs. Sentiment analysis works by using Machine learning and its constituent Deep learning algorithms to create SA models. These models are trained by feeding it millions of pieces of text to detect if a message is positive, negative, or neutral. Sentiment analysis works by breaking a message down into topic chunks and then assigning a sentiment score to each topic. For example, take the following piece of text: “I tried Dispo, a social sharing app. I was really impressed with it. The ‘Rolls’ feature was a little disappointing, but the user experience was amazing” A sentiment analysis tool would break this into topic chunks and then assign a sentiment score to each topic, based on a set scale:

• Dispo social sharing app, I was really impressed = +4
• The ‘Rolls’ feature was a little disappointing = -2
• The user experience was amazing = +3

The system would then sum up the scores or use each score individually to evaluate components of the statement. In this case, there was an overall positive sentiment of +5, but a negative sentiment towards the ‘Rolls feature’.

Tips For Sentiment Analysis: 

• Use SA to track negative sentiment towards your brand. This not only lets you fix your product/service but also enables you to understand how customer sentiment towards your brand changes over time.
• Track key topics that are relevant to your business
• Gauge where your audience spends most of their time, and what type of content they are engaging with, track sentiment across the web, and come up with content that speaks to your audience.
• Purpose-built Sentiment Analysis tools can help you understand your audience better and save you the hassle of experimenting with what works and what doesn't. Investing in one would enable you to focus on making your overall processes better.

Also Read: Importance of Sales Pipeline Management

Applying Sentiment Analysis To Business: 

In essence, Sentiment analysis equips you with an understanding of how your customers perceive your brand. Apart from the CS tickets, live chats, and user feedback your business gets on the daily, the internet itself can be an opinion minefield for your audience. Being able to not just access these opinions, but process them at scale, and get an overall understanding of your market presence, is a key advantage for any business looking to improve their products, selling process, and brand presence.

Here are some important business functions that could use Sentiment Analysis in their operations:

• Sales: Every savvy salesperson has their own theories, instincts, and experiences that they rely on to engage prospects and close deals. Relying on these traits leaves a lot to gut instinct and luck. AI sentiment analysis can help change this, and enable the salesperson to take the guesswork out of their prospecting calls. Sentiment Analysis tools can parse through meeting transcripts and give you the overall tone, and sentiment (positive, negative, or neutral) of the entire call. Moreover, SA tools can help pinpoint keywords, competitor mentions, pricing references, and so much more -- information that could be the differentiator between a salesperson winning or losing a deal.
• Customer Service: According to a study, 32% of customers stop using a product or brand after one bad customer service experience. With the increase in the number of channels customers can reach brands on, the risk of one bad experience becomes multifold. The ability to track brand sentiment across the web can keep CX teams in the know about what’s coming their way. For example: if an aspect of your product has been criticized on an online forum, your customer support team can even use those opinions to put together an emergency plan to tackle them.
• Brand Outlook Monitoring: Besides social media, people talk about brands in blogs, news sites, forums, and product reviews. And while it’s alright to track brand mentions (in terms of quantity), it’s even more important to analyze how they are talking about you. Sentiment analysis can add valuable context to quantitative metrics and help you understand the nuances of customer opinions. You can analyze brand sentiment over time and notice any sudden changes in them. You can also track public sentiment to assess the impact of a PR crisis on your brand and evaluate whether your efforts to handle the situation were successful.
• Market Research: Sentiment analysis can be very useful to analyze your competitors, spot market trends, and conduct market research. You can analyze sentiment in product review sites, social media posts, and community forums about your competition to learn about their strengths and weaknesses. Where do they excel that you may need to work on? Are your competitors not doing something that you can execute to gain more market share? - are some of the questions Sentiment Analysis helps deduce and answer. Also, you can follow specific keywords or hashtags and monitor sentiment around topics that are relevant to your industry. This can help you detect market trends or measure interest around certain topics to gain a competitive advantage.
• Human Resources: It is as important to listen to the voice of your employees, as you do from your customers. Employee productivity directly ties to your business’s revenue. Hence it is critical that you actively source feedback from your employees about the product, company culture, and processes in place. Businesses can use Sentiment analysis tools to analyze internal surveys to weed out organizational and operational issues that are impacting the workforce. This allows your employees to be heard, which is crucial for any company.

Challenges In Sentiment Analysis:

One of the most important challenges around Sentiment analysis is that companies usually struggle with is the accuracy with which they are able to gauge customer and prospect sentiment. Moreover, there are complementary factors like bias and context that make SA a challenge to implement at scale. Sentiment analysis can be difficult simply because machines have to be trained to analyze and understand emotions like humans do. Here are some of the major pitfalls that a business might face while trying to analyze sentiment:

• Irony and sarcasm In a sarcastic text, people express their negative sentiments using positive words. Here is an example of a tweet from an irate customer: “I love how robust their tool is, I probably experience only 5 hrs of downtime a day “ Expressing negative sentiment using compliments can make it tough for SA tools to detect sarcasm and irony, especially without having a good understanding of the context of the situation. The solution for this problem is in training a SA model dataset that not only has to be precise but also needs to be exhaustive.
• Tonality of text: Tone can be difficult to interpret verbally, and even more difficult to figure out in a text. The most important challenge for businesses and brands is to differentiate between subjective sentiment (i.e: I personally don’t like pizza) and objective sentiment (i.e That car is red in color). Brands predominantly look for subjective reviews and opinions on the web, so they can understand how they are perceived among their audience. For example: “I love the tool, but my budget does not” is a text that can be interpreted as “The cost of the tool makes me consider it less”. This is a subjective response that a SA tool can struggle to categorize, and when a large volume of data that contains tonal responses like these are analyzed, things can get tricky.
• Multipolarity: Context is critical. Even if you're speaking to a person, you'd have trouble continuing the conversation if you didn't have context. One of the problems that can arise due to lack of context is changes in polarity. A question like “What do you like the most in our tool” can elicit responses like “Everything” and “Nothing”, whereas the same responses can fit into a question like “What do you dislike the most in our tool” -- the negative in the question changes the context and by extension, the sentiment altogether. In addition to these challenges above, there are some limitations in understanding negation in text, comparative sentences, defining neutral, etc. The good news is, as data and machine learning continue to evolve, sentiment analysis tools are becoming well suited to tackle these issues better. To Summarize, Sentiment analysis is a great way to understand the opinion or feeling of a customer. It has its own set of challenges and limitations but is currently improving at a rapid pace. Nevertheless, sentiment analysis is an excellent way to obtain unbiased opinions from customers and can help improve your business across verticals like sales, marketing, and customer service.

Also Read: Components of Sales Enablement

Frequently Asked Questions:

• Is sentiment analysis ML or AI? Machine learning is a subset of Artificial intelligence. Artificial Intelligence is an overarching term that finds its applications in ML, Deep Learning, Neural networks, and the like. Sentiment Analysis would fall under the ML umbrella since it has a lot to do with identifying patterns and predicting outcomes. 
• Who uses sentiment Analysis? Many organizations and brands deal with customer opinion and feedback on the daily, thanks to social networks, and other customer interaction channels. Human analysts have limited time to process and analyze these data manually, hence Sentiment Analysis is most often used by businesses to gauge audience perception of their brand.
• How accurate is sentiment analysis? Accuracy of a sentiment analysis tool or a model varies across domains and the datasets it's been trained on. But the good news is, sentiment analysis models get better and better with time as they are able to ingest varied data values and learn from them. State-of-the-art approaches have achieved as high as 97% accuracy levels on benchmark datasets.
• Which model is best for sentiment analysis? There are traditional machine learning approaches like Naive Bayes, Logistic regression, and support vector machines that scale really well. However, In recent years, Deep learning has been popularly used in Sentiment Analysis. Deep Learning helps in context-aware sentiment classification. But, the choice of model depends on the business, domain, and the use case you are solving for. 
• Which is the best approach for sentiment analysis? Approach depends on the use case, availability of corpus data, and technology constraints. If annotated corpus data is available, machine learning models can be trained to classify the sentiment associated. If enough corpus data is not available pre-trained Deep Learning models can be leveraged and fine-tuned with domain-specific data for improved performance.

Also Read: How Does Conversational AI Work?

Aviso AI's Sentiment Analysis

Here at Aviso, we are at the frontier of leveraging Sentiment analysis to aid businesses with their sales process. Our conversational intelligence platform powered by Aviso’s AI engine enables sellers to understand their overall meeting effectiveness through metrics like customer sentiment, and keywords - parsed from their meeting transcripts. This includes

• Key metrics and sentiment insights are available as soon as even one call is completed. Models are continuously refreshed
• No need to wait for hundreds of hours of recordings to accumulate
• Our models are unsupervised and will be retrained going forward to be more specific to the context

Moreover, our aspect-based sentiment analysis further augments our state-of-the-art AI and ML models with the ability to understand “positive” and “negative” sentiment in context, and guides sales reps on the next best action to take. Armed with these insights, your salespeople can accurately forecast win rates and go into prospect meetings with the confidence of a champion. Aviso makes it easier for your sales org to Always Be Closing! To understand Aviso’s Conversational Intelligence platform, visit our website. If you’re in the market for a sales forecasting tool, our experts can tell you how Aviso can help achieve your forecasting goals with >98% accuracy, every quarter. Sign up for a free consultation with our expert, and we’ll reach out to you!