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.
Natural language is often vague or highly contextual, making it very difficult for a machine to understand without human help . As such, human annotated data and insight is essential when training a machine learning platform to analyze sentiment and interpret information. Understanding the sentiment of an article is key to gaining insightful, actionable data and providing the topic and trend data that drives relevance.
We traditionally used an algorithm that takes a document and applies sentiment for the entire text. The longer the document, the less accurate the sentiment, and the more skewed the analytics become.
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.
With the new model, we see significant shift of sentiment for every language (55-65% less sentiment overwrites globally!) Media intelligence can now begin to offer us in-depth insight into topic and trend data. As a result, we can adapt our communication strategy to offer our consumers messaging they love. This sentiment analysis now opens up a new world of understanding the social landscape, speaking directly to our audiences and getting ahead of our competitors. And, it can be drilled down to analyse data as small as a keyword or phrase.
Suddenly, we can identify additional opportunities at the top of the sales funnel and use data mining to drive real growth for our brands. We can move beyond measuring basic KPIs like engagement and reach and actually understand
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.
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.