emoji balloon: smiling or grimacing?
PHOTO: Bernard Hermant

Sentiment analysis is the kind of tool a marketer dreams about. By gauging the public’s opinion of an event or product through analysis of data on a scale no human could achieve, it gives your team the ability to figure out what people really think. Backed by a growing body of innovative research, sentiment-analysis tools have the ability to dramatically improve your ROI — yet many companies are overlooking it.

As someone who has worked closely with a range of natural language processing applications, I’ve found the biggest barrier to the adoption of sentiment-analysis tools is the general lack of knowledge around what these tools do, how they work, and their possible effect on your product. 

With such huge market opportunities on offer, this becomes a poorer excuse by the day. If you want your business to reap the rewards of this technology, some basic knowledge is absolutely essential. 

Let’s dive into what sentiment analysis is and how it can help your marketing team drastically improve their results.

Related Article: 5 Drivers of Personalized Experiences: A Walk Through the AI Food Chain

What Is Sentiment Analysis?

At its core, sentiment analysis is about judging the feeling behind a piece of writing. The process involves taking a piece of text, whether a phrase or a full article, and analyzing the emotion the author is expressing. At the most basic level, a sentiment-analysis tool will classify pieces of text as positive, negative or neutral. By way of example, let’s imagine that we’ve collected tweets on a range of political issues, with the goal of analyzing public opinion. Our sample could include the following:

            “I love what Bernie is doing! Vote Democrat!”

            “Four more years of the Republicans … This is the worst.”

So far, so good. Most simple sentiment-analysis tools won’t struggle to tell you that the first tweet is positive and the second is negative. However, human expression is rarely that straightforward. When we speak, we convey a wide range of emotions that sometimes require context to fully understand. This can all happen within a single sentence. Continuing with our theme, consider these examples:

            “Donald Trump’s presidency is one of the best things that’s ever happened to this country.”

            “The Syrian refugee crisis has resulted in the displacement of millions of people.”

            “I pray that God will free us of the fear, hate and suspicion that is dividing our country. His love saves!”

These tweets are difficult to categorize for a variety of reasons. The first tweet could be either genuine or sarcastic but lacks context. It’s also a highly polarizing issue, meaning the tagger has to be careful not to let their own opinions affect their labeling. The second is problematic because it talks about negative events in a neutral manner. It’s unclear if we should consider it neutral reporting or assume the speaker is indeed sad about the crisis. As for the third tweet, the positive affirmations of faith starkly contrast with the extremely negative situation that contextualizes them. It’s not easy to make the decision about which label is most appropriate here.

However, a sentiment-analysis algorithm built on high-quality training data should be able to classify all of the above tweets. It does this by comparing pieces of the text with examples from its training data and its previous experience with similar edge cases. There are several different ways in which this task can be carried out within a sentiment-analysis system. These include:

  • Rule-based approaches, which rely on manually defined rules in a scripted language that incorporate NLP techniques, like stemming or tokenization.
  • Automatic approaches, which build upon machine-learning techniques and frame the task as a classification problem to be solved by neural networks, logistical regression, or other statistical models.
  • Hybrid systems, which combine elements of both approaches.

While these types of algorithms each have their own advantages and disadvantages, they all have a varied range of potential applications that your team can explore.

Related Article: The Next Chapter in Artificial Intelligence: Sense and Sensibility

How Does This Apply to Marketing?

As with all sectors of machine learning, innovation around sentiment analysis is happening at a lightning pace and the scope for its use is vast. As an extremely valuable tool for social media companies, business owners and advertisers, sentiment analysis is already providing insights that help drive effective business decisions, strategies and objectives across a range of sectors. These insights range from the analysis of reviews of your brand and the competition to comparison of your product’s reception in new, international markets.

However, sentiment analysis is also being used in unexpected ways. In what could be a game-changer for marketers, the power of these algorithms can be brought to bear on a range of predictive tasks. From the macro campaign level right down to the micro wording of a landing page, sentiment analysis allows you to fine tune a message for the greatest impact. The financial services sector, for example, is already exploring this. By combining the ability of machine learning to analyze the sentiment of corporate statements and process historical data in real time, financial institutions are increasingly able to rely on AI to make the snap decisions that drive the market. Whether you’re looking to plot long-term trends or figure out how to make a single piece of content have an instant impact, sentiment analysis could add an extra dimension to your efforts.

It’s important to remember the extent to which sentiment analysis improves your business will depend on how far you’re willing to go when integrating it with your systems. Most large businesses that want to reap the full rewards of sentiment analysis actually train their own tools with either private data or data specific to their domain. While this takes longer to build out than simply purchasing a general tool, it’s worth the time and effort. Let’s take a closer look at how you can start to implement this technology in your business.

Related Article: Natural Language Processing Is Hitting Its Stride

How Can You Bring Sentiment Analysis Into Your Business?

For accurate, insightful sentiment analysis, it’s crucial you have a good tool. Many basic sentiment-analysis tools are built into existing platforms, such as Google Insights, Google Alerts or Facebook Insights. Other useful tools include Brandwatch, Hootsuite Insights, Meltwater and OpenText.

If these aren’t enough for you, it may be worth building your own algorithm that is focused on your specific use case. The crucial thing to remember here is that high-quality, human-annotated data is the key to success. The best algorithms are able to draw on human understanding of language within their training data, which helps them to better understand the tone, context and subtle nuance of a piece of writing. Since language is unpredictable and highly susceptible to change, your model’s performance will be judged on the small percentage of edge cases where these apply.

Luckily, you don’t have to label your data in-house. There are a range of online data-annotation services that will be able to provide you with a large volume of clean, relevant data. If you do your due diligence and find a good source of training data, you’ll see a big difference in the quality of your end product. However, there are a few things that you can do to ensure that you maximize your ROI around training data for sentiment analysis. Before ordering your data, consider the following things:

  • Clear instructions: In much the same way as our political examples needed clarification, annotators will appreciate any further guidance you’re able to provide. One crucial thing to consider is whether you require tags in simple positive / negative / neutral categories, or something more complex.
  • Output quality: In sentiment analysis, there is often no right or wrong answer, so it’s difficult to measure accuracy in this way. Instead, it’s often better to use metrics like Krippendorff’s alpha, which look at the consensus between your contributors as an indicator of quality.
  • Number of data points: Often, companies will approach their data providers with hundreds of thousands of data points for tagging. If you only need to train a simple system with limited categories, this is overkill — and the easiest way to ensure your costs balloon out of control. Honesty and clarity around your project will help both you and your data provider to focus on providing you with the best possible dataset for your model.

Related Article: What Data Will You Feed Your Artificial Intelligence?

A Trend to Keep an Eye On

Sentiment analysis is poised to have a big impact on the world of marketing in the near future. By helping to craft brand messages and understand what makes customers tick, these algorithms could multiply the reach and influence of businesses across a range of sectors. With an ever-growing number of solution providers and data services providing all the resources you need to get started, there’s no excuse for being left behind. Those who embrace it in this early stage, either by adopting tools or choosing to build their own, will see their products resonate with consumers in a way that will be the envy of all others in the sector.