They say a good business always listens to its customers.
But what does listening really mean?
Where are people talking about your business to listen in the first place?
And how do you go about not only listening but hearing them- truly understanding them??
These are some of the questions bothering business owners, marketers, business development experts, advertising wings, and other key stakeholders every day. It was not until recently that we had started obtaining answers to all these questions we have been asking for years. Today, we not only can listen to our customers and pay attention to what they have to say about our products or services but take corrective measures, acknowledge, and even reward people who have something valid or commendable to say
We can do this with a technique called sentiment analysis. A concept long-existing, sentiment analysis became a buzzword and then a household name in the business spectrum after the advent and predominance of social media platforms and Big Data. Today, people are more vocal about their experiences, sentiments, and emotions on products and services more than ever and it is on this element that sentiment analysis capitalizes.
If you are new to this topic and want to explore in detail what sentiment analysis is, what it could mean for your business, and more, you have come to the right place. We are sure that by the end of the post, you will have actionable insights on the topic.
Let’s get started
What Is Sentiment Analysis?
Sentiment analysis is the process of deducing, gauging, or understanding the image your product, service, or brand carries in the market. It analyzes human emotions and sentiments by interpreting nuances in customer reviews, financial news, social media, etc. If this sounds too complicated, let’s refine it further.
Sentiment analysis is also considered opinion mining. With the rise of social media, people have started talking more openly about their experiences with products and services online through blogs, vlogs, social media stories, reviews, recommendations, roundups, hashtags, comments, direct messages, news articles, and various other platforms. When this happens online, it leaves a digital footprint of an individual’s expression of an experience. Now, this experience could be positive, negative, or simply neutral.
Sentiment analysis is the mining of all these expressions and experiences online in the form of texts. With a large sample set of opinions and expressions, a brand can precisely capture the voice of its target audience, understand market dynamics and even get to know where it stands in the market among end-users.
In short, sentiment analysis brings out the opinion people have on a brand, product, service, or all of these.
Social media channels are treasure chests of information about your business and with effective simple analysis techniques, you can know whatever you need to about your brand.
At the same time, we have to remove a misconception about sentiment analysis. Unlike what it sounds, sentiment analysis is not a one-step tool or technique that can instantly fetch you opinions and sentiments around your brand. It’s a blend of algorithms, data mining techniques, automation, and even Natural Language Processing (NLP) and requires complex implementations.
Why Sentiment Analysis is important?
From the outlook, it’s a pretty simple giveaway that people have the power to talk about your brand or business online. When they have a certain volume of audience, chances are highly likely that they could influence 10 more people to either trust or skip your brand.
With the internet offering transparency for both the good and bad, it is vital for a business to ensure negative mentions are removed or altered and the good ones are projected for viewership. Statistics and reports also reveal that young customers (Gen Z and beyond) are very much dependent on social media channels and influencers when it comes to buying anything online. In that case, sentiment analysis not only becomes vital but quite possibly a vital tool as well.
What Are The Different Types Of Sentiment Analysis?
Like sentiments – sentiment analysis can be complex; it is also extremely specific and goal-oriented. To get the best results and inferences out of your sentiment analysis campaigns, you need to define your objectives and goals as precisely as possible. There are several parameters when it comes to consumer feedback you can focus on and what you choose can directly influence the type of sentiment analysis campaign you end up implementing.
To give you a quick idea, here are the different types of sentiment analysis parameters –
- Polarity – focus on the reviews your brand receives online (positive, neutral, and negative)
- Emotions – focus on the emotion your product or service kindles in the minds of your customers (happy, sad, disappointed, excited, and more)
- Urgency – focus on the immediacy of using your brand or finding out an effective solution to your customers’ problems (urgent and wait-able)
- Intention – focus on finding out if your users are interested in using your product or brand or not
You may either choose to use these parameters to define your analysis campaign or come up with other super-specific ones based on your business niche, competition, goals, and more. Once you have decided on this, you could end up subscribing to one of the following types of sentiment analysis.
This method determines the emotion behind using your brand for a purpose. For instance, if they bought apparel from your eCommerce store, they could either be happy with your shipment procedures, quality of apparel, or range of selections or be disappointed with them. Apart from these two emotions, a user could face any specific or a mix of emotions in the spectrum as well. Emotion detection works on finding out what that particular or a range of emotions is. This is done with the help of machine learning algorithms and lexicons.
One of the shortcomings of this type is that users have a multitude of ways to express their emotions – through text, emojis, sarcasm, and more. Your model should be highly evolved to detect the emotion behind their unique expressions.
A more direct form of analysis involves finding out the polarity associated with your brand. From very positive to neutral to very negative, users could experience any attribute with respect to your brand and these attributes could take a tangible shape in the form of ratings (e.g. – stars based) and all your model needs to do is mine these various forms of ratings from diverse sources.
Reviews often contain sound feedback and suggestions that could drive your business growth in the market by letting you uncover loopholes that you never knew existed. Aspect-based sentiment analysis takes you a step further in helping identify them.
In simple words, users generally point out some good or bad things in their reviews apart from ratings and expressing emotion. For instance, a review on your travel business could mention, “The guide was really helpful and showed us all the places in the region and even helped us board our flights.” But, it could also be,” The travel desk associate was extremely rude and lethargic. We had to wait for an hour before we got our itinerary for the day.”
What lies beneath the emotions are two major takeaways from your business operations. These could be fixed, improved, or recognized through aspect-based analytics.
This is the assessment of sentiment across diverse languages. The language could depend on the regions you operate, countries you ship to, and more. This analysis involves the use of language-specific mining and algorithms, translators in the absence of it, sentiment lexicons, and more.
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How does Sentiment Analysis work?
Sentiment analysis is a blend of diverse modules, techniques, and tech concepts. Two major deployments in the spectrum of sentiment analysis include NLP and machine learning. While one helps in the mining and curation of opinions, the other trains or executes specific actions to uncover insights from those opinions. Based on the volume of data you have, you could deploy one of the three sentiment analysis modules. The accuracy of the model you choose immensely depends on the volume of data so it’s always best practice to pay attention to it.
This is where you manually define a rule for your model to perform sentiment analysis on the data you have. The rule could be a parameter we discussed above – polarity, urgency, aspects, and more. This model involves the integration of NLP concepts such as lexicons, tokenization, parsing, stemming, tagging parts of speech, and more.
In a basic model, polarized words are defined or assigned a value – good for positive words and bad for negative words. The model counts the number of positive and negative words in a text and accordingly classifies the sentiment behind the opinion.
One of the major shortcomings of this technique is that instances of sarcasm may be passed off as good opinions, skewing the overall functionality of sentiment analysis. While this can be fixed by building advanced models, the shortcomings nevertheless exist.
This aspect of sentiment analysis works completely on machine learning algorithms. In this, there is no need for human intervention and set manual rules for a model to function. Instead, a classifier is implemented that evaluates the text and returns results. This involves a lot of data tagging and data annotation to help the models understand the data it is being fed.
The most accurate of the models, hybrid approaches blend the best of both worlds – rules-based and automatic. They are more precise, functional, and preferred by businesses for their sentiment analysis campaigns.
What Does Sentiment Analysis Mean For Your Business?
Sentiment analysis could bring in a wave of discoveries as far as your business and its stance in the market is concerned. When the ultimate purpose of a business’s existence is to make the lives of customers easier, listening to them will only help us roll out better products and services and in turn, take our business forward. Here are the key takeaways on what sentiment analysis could do for your business:
- it immensely helps in monitoring your brand’s health in the market. From a single dashboard, you can quickly understand if your brand health is good, neutral, or depleting.
- It helps you manage your brand reputation better and quickly address ORM concerns and crises
- Supports the development of better marketing campaigns by letting you understand the pulse of your audience and tapping into it
- Competition analysis can be optimized through sentiment analysis to significant extents
- Most important of all, customer service can be improved for more satisfaction and quick turnarounds
Sentiment Analysis Use Cases
With such a powerful concept in hand, you are just a creative decision away from implementing the best use case of sentiment analysis. However, there are several market-tested and approved use cases already running today. Let’s look at a few of them briefly.
Sentiment analysis is a great way to monitor your brand online. Currently, there are more channels through which customers can express their opinions and in order to maintain a holistic brand image, we need to implement Omni-channel approaches to monitoring. Sentiment analysis can help our business spread wings across forums, blogs, video streaming websites, podcast platforms, and social media channels and keep an eye – or rather an ear – out for brand mentioning, reviews, discussions, comments, and more.
Social Media Monitoring
It takes as little as a thousand people to make a hashtag trending. With so much power vested with social media, it only makes sense that we listen to what people have to say about our business on social platforms. From Twitter and Facebook to Instagram, Snapchat, LinkedIn, and more, sentiment analysis can be done across all platforms to listen to criticisms and appreciations (social mentions) and respond accordingly. This helps our business engage better with our users, bring in a humane approach to operations and connect directly with the most important stakeholders in our business – our customers.
Sentiment analysis is a great way to understand the market, its loopholes, the potential, and more for our specific needs. With precise market research, it makes purposes like expansion, diversification, and the introduction of new products or services more effective and impactful. We could predict and assess trends, understand market dynamics, realize the need for a new product, understand the purchasing power and other attributes of our target audience, and plenty more through sentiment analysis.
How machine learning is used in sentiment analysis?
As we mentioned, sentiment analysis is a complex concept and when you have large datasets, you cannot help but think that automating the entire process might be the best way to approach it. Of course, if you are deploying an automatic approach to analyzing sentiment, it’s important to precisely train your machine learning model for accurate results.
This is where the complexities arise. The data you feed has to not just be structured but tagged as well. Only when you tag data that your model can understand the sentence structure, parts of speech, polarized words, context, and other parameters involved in a sentence. For that, you need to primarily work on tagging volumes after volumes of data.
When you tag your data, your artificial intelligence or model understands the different aspects of texts and autonomously works on understanding the sentiment behind the data you feed in. You can train your data by annotating specific portions of your texts to help the machine identify what to focus on and learn from that particular parameter. You also need to add metadata to further define the identifier.
If you are planning to annotate your data in-house, you need to first have massive volumes of data in hand. Once you have it, you can use the Shaip platform to annotate your data. However, this process could be complicated as you need to either dedicate your resources to this work or ask them to go the extra mile and get the job done.
If your time to market is coming up very soon, and you need to seek external sources for your data annotation needs, resources like us at Shaip can save the day. With our expert data annotation processes, we ensure your machine learning models are fed the most precise dataset for precise results. Our team annotates data based on your needs and requirements to provide a goal-oriented result. Because this is a time-consuming and tedious process, we suggest getting in touch with your data annotation requirements for sentiment analysis training.
Reach out today.