Social media is filled with opinions. Be it praise, a complaint or a preference, there is no need for mind readers in the 21st century. We put all our inner thoughts out there – in the digital sphere.
Customer service has changed. Rather than writing or emailing the head offices directly, complaints and feedback are now scattered online across blogs, forums, reviews and social media.
Interactions with your customers, stakeholders and the general public are now more visible. The way you respond, your timeliness and your humanity are now judged on a public stage, with many social media, brand, PR, marketing and customer experience professionals kept awake at night by the prospect of missing something.
Due to the noise and volume of our social media usage, it has become impossible to monitor this feedback manually.
Understanding how your consumers perceive your brand is integral, but attempting to track this using localised searches and traditional market research methods is a huge drain on your time and resources. The solution is understanding emotion, perception and opinion quickly, by using sentiment analysis.
Table of Contents
What is Sentiment?
Sentiment is a metric, which was designed to provide helpful insights into how audiences feel about and perceive a brand, particularly on social media platforms and in editorial news media. This marketing metric is used to help brands manage and protect their reputations, while monitoring the online environment. However, monitoring sentiment can also show you how your competitors are perceived by your target market or how people feel about your latest influencer marketing campaign, in real-time. In fact, you can use it for everything from movie reviews to news coverage – anything covered on social networks.
Other applications include, but are not limited to, understanding your industry, identifying negative sentiment, and addressing it for better brand perception. This in turn can help drive an effective business strategy and give your business more room to grow. Sentiment analysis determines whether a text is positive, negative, or neutral by extracting particular words or phrases, through opinion mining – and serves as a key component of any advanced analytics, despite its reputation as an alternative metric.
And, in a world where the media, and in particular social media insight, dominates – looking at a dataset like this can really help set your brand apart from the competition and drive your content strategy.
Sentiment Analysis Methods: Key Terms and Definitions
Before we get into the why and how of sentiment tracking, let’s take a look at a few key terms.
Sentiment analysis system
This is a system created from a combination of natural language processing and machine learning. It is pre-trained to analyse text and assign weighted scores to social media posts and other forms of content online.
Analysis at a phrase level
This refers to analysing sentiment from text phrases as opposed to the overall messaging. This content-based analysis can also be broken down into sentences.
Developers use these code languages to trigger specific tasks and reactions when users interact with a site or app. Some languages are specifically suited for AI environments.
These tags are used to get specific about analysing text. They filter out words based on their part of speech in a sentence, i.e. an adjective or negation – both of which are important in determining human feeling from expression.
Semi-supervised sentiment classification
In short, semi-supervised classification can use unlabelled data from posts or content online to understand the opinions, feelings and views expressed.
Sentiment analysis model
This model is used to analyse a text string and determine how the author feels, depending on the options you “give” it as classifications. For example, these types of models can determine whether users feel happy, sad or frustrated in an email – if the developer sets the classifications to happy, sad or frustrated based on certain criteria.
Results are typically split into positive, neutral or negative words or phrases – but in some instances, you can extract data that’s simply split into neutral responses or “polarity” responses because they greatly separate two types.
A sentiment score is typically based on a range of -1 to 1, with -1 indicating negativity, 0 – neutrality and 1 – positivity.
Rule-based systems look at an initial set of rules and data specifics to perform certain actions in natural language processing, i.e. assigning a sentiment score during text analytics.
These are the categories of words associated with different types of feelings or emotions, i.e. words like “great” would align with a positive category of terms.
This advanced form of text analytics looks at sentiment associated with different aspects of a product or service, and during natural language processing, it automatically assigns sentiment to specific topics. Machine learning is part of what makes this possible.
Sentiment analysis API
An Application Programming Interface (API) is the bridge between two applications that allows them to interact. It essentially allows them to speak to each other in order to perform complex tasks. So, for example, if you’re using a social media app on your phone an API is the tool that allows you to interact with it on that interface. A sentiment analysis API like Meltwater’s will provide you with all the sentiment data you need.
This model allows for looking at words in terms of their structure in NLP. So instead of looking at the grammar of a sentence, it can be used to pull out words that match tags and can help ascertain how often a word or sentiment type appears.
Why Do We Need To Understand Positive, Neutral and Negative Sentiment?
For social, marketing, communications and PR professionals, understanding your audience gives you the necessary context to create meaningful campaigns and prevent impending crises. As our primary task lies in engagement, persuasion and reputation management, sentiment analysis should be at the cornerstone of every content-based strategy.
With conversations around brands happening primarily on social media, you have no choice but to stay on top of emerging trends in the virtual world. Plus, there’s the added benefit of the rich psychographic and behavioural data you’ll come across along the way.
How your brand responds to an impending crisis could make or break your reputation. However, as we’ve touched on, most teams don’t have the time or resources for opinion mining and running sentiment analyses, manually – and even if they did, it still wouldn’t yield very accurate results. It takes time to filter various pieces of text, identify the type of sentiment associated with those texts, organise the data according to relevant criteria and finally produce a meaningful report.
This is why it’s important to use AI-powered sentiment analysis tools, to achieve a more efficient and insightful outcome. These make use of data science techniques, using algorithms that identify sentiment from a large dataset almost instantaneously. Also, the artificial intelligence (AI) technology that facilitates these actions is constantly improving and, combined with machine learning, has become a powerful mechanism for understanding human emotion online.
How Sentiment Analysis With AI Works
AI uses natural language processing (NLP) with a natural language API and machine learning techniques to automatically detect the sentiment of published text. NLP converts human language into a dataset that machines can understand.
Machine learning then takes over to classify the text after it’s been processed. Data patterns are identified in order to make meaningful predication as the software continuously “learns”. It’s trained to classify sentiment from text into different categories based on association (one method).
One practical application of how AI works can be seen in the buzz about UK’s most popular tabloid newspaper, Metro, wrongly captioning the names of two members of the Little Mix girl group. Needless to say, this conversation ended up on Twitter.
Once the public realised the mistake, the story began to take a life of its own under the #DoBetter hashtag, with social media users demanding an apology, particularly for mistaking two women from racial minorities for one another. The newspaper had to quickly apologise for the mishap.
With AI-based sentence-level sentiment analysis, Metro would have been automatically notified as soon as mentions began to increase in number, without any human intervention. In addition, they could also identify any other secondary issues that arose and aim to address them to avoid a future crisis.
Why Are Sentiment Analysis Tools Important?
As shown in the Metro example, one negative opinion of your brand can create an online storm that includes both an aspect of opinion and emotion. Regardless, it can be quite difficult to recover from, even if expertly managed by the best of PR teams. This is because of the ability of a negative tweet, for example, to go viral within a few minutes.
By using sentiment analysis tools to monitor how your audience feels, your organisation can control the emotions and conversations around your brand more effectively. Also, this way you won’t be responding reactively by trying to douse the flames at short notice. The fact remains, consumer opinions are crucial to how you represent your brand in the media and strategise for more effective relationships with your customers.
Manage your reputation
Like it or not, your reputation is one of the most valuable assets your business has. As Warren Buffet once said :
The online world has millions of individuals continuously speaking about companies and their products and services. These discussions mainly happen on social networks, e-commerce and product review sites, blogs, and other discussion forums.
You can control the narrative much better with both detailed and overall sentiment analysis, making sure your brand is seen in a positive light.
Monitor customer feedback
When it comes to products and services or your ability to provide a good customer experience, consumers are bound to have an opinion. If they have a negative experience in particular, there is a good chance that it will be shared online, usually social media.
Sentiment analysis helps you track reviews, on Google Business directories or individual product/service pages, and identifies the tone of the sentiment. Using this tool, your brand can address the complaints. On the other hand, positive experiences that are shared can give valuable feedback on what you are doing right, and provide extra marketing collateral.
Prevent a crisis
Crises usually occur when we least expect them, and with no contingency plan in hand too. With regular or constant sentiment analysis, you’re better prepared.
A dissatisfied customer voicing their negative opinion about your brand on social media can quickly get support by peers. Even worse, media outlets can pick up on negative sentiment before you do and before you know it, you’re trending online and the news. Sentiment analysis can alert you as soon as a potential crisis is lurking which allows you to address it in time.
For research purposes
Media monitoring tools filter trillions of datapoints, coming from the billions of Internet and social media users on a daily basis.
You can draw valuable insights from a large dataset on specific industry topics, consumer preferences, product features and competitors. One of the best parts about this is that the information is publicly available while accessing it from the comfort of your office chair (or couch, if you’re part of the work-from-home crew). This will, in turn, allow you to create more meaningful PR and marketing campaigns.
With tools like the Meltwater sentiment analysis API, sentiment analysis and other media monitoring solutions, you can cut out the need for endless filtering and hours spent creating reports and other forms of strategic documentation.
Adapting Marketing Strategies through Innovation
The Solution: Perfecting Natural Language Processing with Media Intelligence
Moving away from document-based sentiment analysis to sentence-based sentiment is a more accurate way to gain useful marketing insights. This means each sentence of an article or post is evaluated individually and gets its own sentiment score (positive, negative, neutral communication).
For the document-based sentiment, the sentiment is then calculated based on the sum of sentence sentiment. Head on to our Engineering Blog to learn more from our data scientists on how sentiment is calculated.
Once you understand the value of sentence-level analysis as part of your KPIs, you can begin to drastically affect decision-making in your organisation and make it an important part of your media analysis.
However, the major innovation of the new sentiment comes with the customer feedback loop. Meaning every time a customer overrides a sentiment score in the platform, we feed this data back into our models to help retrain and improve its accuracy. I doing so, our analysis becomes more powerful.
Let’s look at an example. Let’s say you were looking at sentiment analysis in it’s earlier forms. You might see that your audience sentiment was negative because media intelligence used to analyse it at an overall level. However, after the updates, you might find that the same text was determined to have positive sentiment. Why? Because when you analyse it at sentence-level, you get a more accurate reading which is then combined to give you a more accurate reading overall.
Today, media monitoring software has this ability, thanks to advancements in technology. Now it is time to adopt this technology in your social media planning. In doing so, you can create a sentiment analysis system that affects everything you do in marketing.
The Future of Sentiment Analysis
As our model, and the technologies we use, continues to grow, our world-class data science team is continuously seeking new and intuitive ways to help improve sentiment analysis. One example they have already highlighted is Entity Based Sentiment. This seeks to determine the sentiment of certain people, companies, or other entities within a document, adding a new complexity of data interpretation to help drive brand-related conversation online.
In time, global adoption of this technology will allow advertisers, marketers and PR professionals alike, the opportunity to monitor positive and negative sentiment as a norm. The sooner you can adopt this technology and gain a competitive edge, the sooner you can improve your social media management, identify new opportunities, improve your content, manage your reputation and understand what you’re doing right with your consumers. You’ll be able to decide whether or not you’re speaking to the right audiences about the things they care about, instead of just going with the basics and forgoing the social listening that will set your brand apart.
Getting Started With Sentiment Analyses
The sentiment analysis API from Meltwater gives you the capability for in-depth data analytics and deep learning that can transform decision-making in your business, using big data in a practical and meaningful way. Speak to us to find out how we can help you start to understand the emotions and opinion of consumers towards your brand and direct competitors.
Our sentiment analysis tools can transform the way in which you understand your key markets and help you up your strategic game. For a free demo, simply fill out the form below.