Sentiment analysis is a subfield of natural language processing (NLP). It is used in determining and classifying emotions or attitudes within text data using NLP algorithms. It allows companies to identify customer sentiment towards products, brands, or services through a vast corpus of unstructured text data available from social media, user reviews, chatbots, forums, and news.
In a previous post, we explained the technique’s method and took a high-level look at its applications. In this post, we’re going to explore in more detail some examples of sentiment analysis in the real world, looking at how companies and organizations have successfully deployed the technique to help them achieve their goals. If you want to integrate sentiment analysis in your business, talk to our team to get started.
Before we analyze the real-world use cases in more detail, let’s clarify some of the key evaluation metrics necessary to assess the effectiveness of a sentiment analysis model:
Rules of thumb:
Aylien provides an AI-powered text analytics API which allows businesses to generate insights from the vast amounts of world news. A few applications of their API:
The following example from the Aylien research blog highlights the use of ELSA:
Consider a sentence with three different sentiments expressed about three different entities — “Jeb Bush is ok, but lyin’ Ted Cruz is the worst. He’ll never be as great as your president, Donald J Trump”.
Document-level sentiment analysis would only return a single, negative sentiment. ELSA managed to identify the entities and return a sentiment for each, showing a negative sentiment for Ted Cruz, neutral for Jeb Bush, and positive for Donald J Trump.
The team went on to perform ELSA on coverage of the Google I/O conference and tracked the sentiment around 20 different entities, illustrating a broader and more detailed application of the technique.
Learn more about the process and discoveries at Aylien research blog.
A sustainability report is a report published by a company or organization that covers the economic, environmental, and social impacts caused by its everyday activities. A sustainability report is vital for communicating sustainability performance and impacts — whether positive or negative. One of the departments at KPMG, a top auditing company, read client’s sustainability reports to provide an opinion on whether they can be published. By Global Reporting Initiative standards, the report is required to be balanced, i.e., reflects both positive and negative aspects of a company’s performance so that stakeholders can make a good assessment of the performance.
The issue is that the report is verified only by a single person in the sustainability department, and it is a matter of their opinion whether the report is balanced or not. Thus, the task was to make this balance measurable (quantitative) for the clients by leveraging sentiment analysis algorithms.
A frequent challenge we see at super.ai is handling nuance in data labeling, and this was a problem faced by the team at KPMG, as even negative statements were worded positively:
Companies (shockingly) do not tend to use language like “useless product, waste of money” in their annual or sustainability report, but rather discuss ‘challenges’ and ‘vow to do better’.
Due to this, they couldn’t use existing sentiment analysis solutions or models, as they were trained on the wrong kind of data. Thus, they obtained 8,000 newly labeled “sustainability sentiment” sentences.
They tried the following methods for sentiment analysis with little success:
However, all these models disagreed. And most positively worded negative sentiments were still being predicted as positive.
To overcome these problems, they needed a model that could understand the context better. Hence, they used BERT or Bidirectional Encoder Representation from Transformers. It is a pre-trained language representation model introduced by Google. More specifically, they used the BERT base (12 layers in the network) model instead of BERT large (24 layers).
They found huge improvements in the per-class (negative, positive, and neutral) classification accuracy, precision, and F score compared to the previous methods.
Learn more here.
The urban-planning department of Brazil recruited McKinsey, a leading consulting firm, to develop a tool called City Voices that captures and analyses citizens’ sentiment across key aspects of city life to help leaders understand what matters most to their constituents.
“McKinsey conducted a thorough study of different citizens and journeys, identified a list of more than 150 different metrics, and then whittled them down to a key 30, which were then subjected to sentiment analysis algorithms to arrive at the insights that could underpin public policy.”
Learn more in McKinsey’s Voices on Infrastructure issue.
Do you know any creative or powerful examples of sentiment analysis in the real world or need help using sentiment analysis in your business? Reach out to us and let us know.
We’re going to continue exploring various techniques and use cases in the world of NLP over the coming weeks, so stay tuned to our blog to learn more.