They say your brand is what your customers say it is. However, with social media becoming one of the vital marketing platforms for businesses of all types and sizes, this mantra has slightly changed – your brand is what your customers say it is on social media platforms.
All businesses that want to improve their social media presence and brand image must analyze and decode what customers say and understand their opinions, emotions, and attitudes. Guess what the best way to do it is? Social media sentiment analysis.
In this article, we will take a detailed look at social media sentiment analysis – what it is, what it says about your customers and your brand, how to extract sentiment from social media conversations, how to use the data you obtain from it, and more.
What Is Social Media Sentiment Analysis?
Social media sentiment analysis, also called opinion mining, is a type of sentiment analysis in which you collect and analyze the information available on various social platforms to learn how people perceive your brand, products, or services. It examines social conversations and user-generated content, such as brand mentions, comments, and posts, to reveal the feeling within them.
The process is much more than tracking quantitative metrics, which merely measure the number of comments, reactions, and reviews. Social media sentiment analysis uses Natural Language Processing (NLP) and machine learning to decipher a text and interpret its context, thus revealing whether a post or comment is positive, negative, or neutral.
Why Should Businesses Need Social Media Sentiment Analysis?
According to feedback surveys, about 71% of SMEs use social media for sales and marketing purposes. While businesses interact with users through posts, users respond to them through likes, shares, brand mentions, reviews, and the like. Statistics reveal that more than 200 million users visit at least one business profile per pay, and about 80% of users have tweeted something about a brand at least once.
Almost all social platforms let you measure and track crucial metrics like reach, impressions, audience growth rate, video view rate, virality rate, click-through rate, etc. These metrics help you assess how strong your social presence is and how good your marketing strategies are.
That said, such metrics make brands obsessed with quantitative data, such as the number of mentions. They overlook the emotions, opinions, attitudes, and feelings comments and brand mentions carry. It is one thing to count the likes, brand mentions, and posts, but understanding the sentiment behind them is a different game altogether.
That is why businesses need sentiment analysis. It tells you what quantitative metrics do not reveal – how social media users perceive your business and your products/services.
What Exactly Does Social Media Sentiment Analysis Do?
All social conversations wear one sentiment or the other – either there is a positive sentiment, a negative sentiment, or an absence of both. Sentiment analysis examines a social conversation, such as a comment or a mention, and identifies its polarity — whether it contains a positive, negative, or neutral sentiment.
Depending on how advanced and feature-rich the tool you use, it might also identify the context, the exact emotion expressed by the customer, and the specific aspect to which the sentiment is attached.
Confusing? Here is an example to help you understand the process better:
A sentiment analysis platform will use machine learning and NLP techniques to examine the text, its context, and terms and modifiers like wrong, mess, etc., and identify it as a negative comment. It will further analyze it to detect the underlying emotion – disappointment or frustration, to be used for feedback.
That is not all. Here, the customer seems to have problems with product pictures and descriptions. Advanced analytics platforms will also identify those specific aspects with which the customer has a negative sentiment.
Though you can do manual analysis, it is a cumbersome task that consumes your time, energy, and resources. The good news is that you can automate the whole process with a sentiment analysis platform that uses Natural Language Processing (NLP) and Artificial Intelligence (AI) to tap into customer emotions.
The process of sentiment analysis may vary slightly from one tool to another. However, it typically involves a few steps.
Step 1: Data gathering
The first step is to gather data from various social platforms using APIs, scraping tools, and other methods. Most tools look for posts of your brand or business over a specific timeframe and collate them for analyzing.
Step 2: Text cleaning
The collected data is often noisy, unstructured, and cluttered. To make it suitable for analyzing, you must clean it first by removing punctuations, stop words, repetitive texts, etc. Text cleaning generally comes as a feature of many sentiment analytics platforms.
Step 3: Data analysis
The third and most crucial stage involves analyzing the collected and cleaned data using sentiment analysis algorithms. The focus of the analysis may vary depending on your business requirements and the tools you use.
Standard sentiment analysis involves classifying the data based on polarity – whether a text is good, negative, or neutral.
Fine-grained or graded sentiment analysis tools will allow you to categorize the content more precisely, such as:
Sentiment detection is also a feature in many advanced analytics tools. It helps detect emotions – anger, frustration, happiness, sadness, anticipation, etc.- within texts.
Aspect-based sentiment analysis goes one step further. It assigns the identified feeling to certain pre-defined aspects, features, and topics. For instance, if a customer says, I loved the features of the phone, but it is very pricey, the tool will assign a good sentiment to phone features and a negative one to the price.
Step 4: Data visualization
Once the tool analyzes data and detects sentiments/polarity, it turns the results into graphs and charts that are easy to understand. The reports will show you the surges in good sentiments (called peaks) and surges in negative sentiments (called valleys) within the timeframe you have initially chosen. Most tools will also show the results based on overall sentiment, sentiment over time, sentiment by rating, and sentiment by topic.
Benefits Of Social Media Sentiment Analysis
More often than not, brand mentions and comments are forms of honest feedback. It offers valuable insights into how people interact with your products/services and creates opportunities to manage your reputation. Analyzing social conversations and the feelings within them helps your business in more ways than one. It helps you:
Engage with customers the right way
Surveys show that 89% of users choose a business that responds to all their reviews and comments. However, many brands make the mistake of only responding to negative posts. They pay little or no attention to happy or neutral posts.
Social media sentiment analysis is a great tool to help you understand the nature of the comment and create an engagement strategy based on that. For example, you can:
Thank and appreciate a customer who left a positive comment
Address a negative comment using the right support team
Respond to a neutral comment in such a way that you can further engage the customer
Understand customer trends
Customer trends change over time, especially during specific periods, such as holiday seasons or crises like the COVID-19 pandemic, climate change, or recession. For instance, reports indicate that about 85% of clients have become greener in their purchasing choices. Likewise, surveys also show that the coronavirus pandemic has led people to spend more on fitness equipment and health supplements and reduce their spending on big-ticket items.
To offer your products or services according to the latest customer trend, you must learn and keep up with these changes. Social media sentiment analysis shows what customers consider a priority during a particular time and helps you change your marketing and content strategies accordingly.
Personalize customer experiences
According to the latest statistics, personalization can remarkably increase the likelihood of purchases, repurchases, and recommendations. About 76% of customers are more likely to purchase products or services from businesses that personalize the experience, while 78% are likely to recommend or repurchase.
Tracking social media data using sentiment analysis gives you insights that you can use to personalize your social media marketing strategy, content curation strategy, marketing campaigns, and retargeting ads.
Offer better customer support
Leaving comments or brand mentions to draw the attention of businesses has become the new normal. Statistics reveal that about 33% of users prefer social media over phones to contact brands. Surveys further show that 32% of people who reach out to businesses through social media expect a response within 30 minutes, while 42% expect a response within 60 minutes.
Social media sentiment analyzing is a great way to increase the quality of your customer service functions by capturing the pain points. For starters, it alerts your customer support teams regarding the issues clients face and helps react to those issues. Your teams can also use insights from sentiment analysis to proactively reach out to users, which enhances your brand reputation by several notches.
Detect and manage a crisis
Often, an influx of negative posts may indicate a brand or PR crisis. Monitoring your comments using a sentiment analysis platform is a great way to detect them early and manage them before it gets too late. The more time you take to respond, the more media coverage the issue will get and the more it will escalate.
Experts often cite the United Airlines crisis as a case in point. In 2017, videos surfaced on social media showing security officers removing a passenger violently from one of the planes of United Airlines due to overbooking. It had a snowball effect - the airline was mentioned 2.9 million times on social media, while their positive mentions declined from 90% to 30%. Due to their initial response, which seemed to justify the act, the company continued to receive backlash for the next 48 hours until they issued a proper apology.
Sentiment analysis can help brands in such situations to quickly assess the feeling of the public and come up with responses that ease the backlash.
Tweak your content and products/services
Comments are a treasure box that reveals whether your social media content is engagement-worthy. A sentiment analysis tool would show the spikes in your positive, negative, and neutral sentiments. It tells you what makes a customer tick, and you can use this insight to curate content and social posts that elicit more happy responses.
Likewise, the polarity of the sentiment may also reveal interesting facts about how user-friendly or functional your product or service is. Based on this, you can improve your services and products and change the game. More often than not, such data would also give ideas for developing new products or services.
Understand the competition
You can use social media sentiment analysis platforms to monitor your brand; you can also use them to monitor your competitors. It will give you valuable insights into what customers like or dislike about the products and services offered by your competitors and shape your marketing and content strategies accordingly.
Use positive user-generated content for ads
User-Generated Content (UGC), such as brand mentions, reviews, and posts, is also a great way to advertise your products and services. Once you have identified posts with positive sentiment, you can reshare them or incorporate them into your advertising strategy.
Statistics reveal that consumers consider such UGC to be 2.4 times more authentic than brand-created content. Further, if you feature UGC in your ads, they will likely fetch 73% more positive engagement on social platforms than traditional ads.
Attract more customers
According to the Global State of Digital 2020 report, about 75% of internet users search for brands using social media. It means that a mention or comment you receive is not just a mention or comment – it is a de facto review that might help another customer to make a purchase decision. You would not want negative mentions to come in the way of your brand and clients, would you?
Social media sentiment analysis helps you identify negative comments and address them before issues escalate. Likewise, you can use happy comments and mentions to endorse your brand image. That way, you will come across as a proactive brand for a new customer who skims through your social media handles. It gives them the confidence to purchase your products and services.
Social Media Sentiment Analysis Examples – Texts and Analysis
Here are a few real-life examples that will help you see how social media sentiment analysis works:
The comment and mention above show that the customer is happy with the product. A sentiment analysis tool will extract adjectives and modifiers like excellent and loved to indicate that the feeling is positive.
Here, as you can see, the customer expresses two feelings – happiness about the quality of the product and disappointment about the delivery time taken. Doing an aspect-based sentiment analysis will assign two sentiments to these two aspects. It will enable the customer support person to address the issue, which is late delivery.
This post also contains positive and negative feelings. It tells the brand that the multiple playlists option was one of their best features (positive), but the customer is sad that it is no longer available (negative). By performing sentiment analysis of such mentions, brands can understand the pulse of customers and tweak their product/service offerings.
This comment/mention shows how the language used on social media can be complex – though the customer uses the word congratulate (positive sentiment), it is sarcasm. The text also uses strong negative words such as worst, badly, incompetency, and ignorance. An AI-enabled sentiment analysis platform will place such texts into context and extract underlying feelings.
Here, the customer expresses neither positive nor negative sentiments. Instead, they raise a question regarding the real-life, practical usability of the product. In other words, the feeling of the post is somewhat neutral.
However, it still gives the brand an opportunity to step in and engage the customer by explaining more about the product or showing them how the product is a good choice due to XYZ features.
Start Analyzing Social Media Sentiments Today with Lettria
All brands want to know what customers think and feel about them – if there were a magic wand that would open a direct doorway to customers, all organizations would go for it. Well, there is no such wand, but there are social media feedback and sentiment analysis platforms like Lettria.
The sentiment and customer feedback analysis platform we offer enables you to enhance your social media presence and engage with your clients more meaningfully. With a no-code AI platform that uses NLP automation, we analyze your textual data and feedback with the utmost accuracy. Our platform is ideal for businesses of all types and sizes, even organizations without a dedicated technical team, data scientists, or developers.
If you are a business that wants to make the most of your social media comments and feedback, now is the time, and Lettria is the right choice.