Sentiment analysis explained: How organisations are using sentiment analysis to respond in real time
By Sheng Zhu, Managing Consultant at Credera (UK).
Sentiment analysis involves using a series of machine learning (ML) and AI techniques to identify and quantify human beings’ emotional reactions towards certain subjects.
Originally, sentiment analysis referred to ‘text’ sentiment, which is predominantly powered by natural language processing (NLP) solutions and predicts positive and negative sentiment scores. However, by leveraging the latest AI technologies, sentiment analysis now refers to a significantly more sophisticated domain such as facial, expression, voice, and microbody language sentiment and emotions.
Whilst it has been attracting a lot of close interest and investment for commercial perspectives or political motivations, any breakthroughs of sentiment analysis are essential to unlocking the full possibilities of Artificial Intelligence (AI).
Potential use cases for sentiment analysis
While sentiment analysis solutions are still rapidly evolving, there are already a number of potential use cases across a wide range of industries. Based on Credera’s vast experience in this domain, we have summarised some of the most common examples of sentiment analysis implementation and its associated benefits below:
Retail sales forecasting
Beyond the use of sentiment analysis to protect a brand’s reputation, it is also being used within the retail industry as an advanced sales forecast supporting tool. Instead of tracking a brand’s reputation, a number of organisations have gone the extra mile and are now tracking the market sentiment against each of their product’s portfolios. The sentiment scores of each product can truly reflect market trends and even market demand. By leveraging other data such as historic sales, retail organisations can benefit from swift and ‘Agile’ solutions that enable them to react and adjust the retail forecast, pricing, and supply strategy in a timely and scientific manner.
Capital Market listening
Given the nature of Capital Markets and their sensitivity to market sentiment, we have seen a significant amount of investment and initiatives in investment banking, fund managers, and private equities. These organisations are not only monitoring the entire market but are also interested in public sentiment towards segment or even specific commodities. Some organisations have also developed sophisticated market listening and decision supporting tools, allowing them to react swiftly to material events, hedge investment risks, and even lead the market sentiment.
Potential policy impact assessment
Within the public sector, there are often a series of tests carried out to measure the potential reaction of the public prior to a policy being formally announced. In this instance, a statistical or scientific method often plays a crucial role.
The famous ‘Policy Simulation Model’ (PSM) created by Department for Work and Pensions (DWP), uses Micro Simulation techniques to assess the potential impact of a policy. However, by assessing the real public sentiment towards a policy, the Potential Policy Impact would record more accurately, helping to avoid repercussions or even a U-turn to an established policy within British government departments.
Brand protection and service improvement
Recent years have seen a significant advancement in the use of sentiment analysis to deliver brand protection and service improvement too. In addition to general sentiment scoring on social media texts, organisations have started to utilise advanced natural language mining solutions and graph database solutions to uncover any actionable insights, providing answers to questions such as ‘what are the key factors to a positive sentiment?’ and ‘who was the initial influencer of a wave of negative comments?’
Other organisations have also taken advantage of some of the latest AI technologies such as ‘Computer Vision’ and ‘Voice (speech) recognition’ to really enhance their existing sentiment solutions in a much wider context. Customer sentiment in recorded service calls is now being used to provide an additional assurance (or evidence) for customer service quality management and improvement.
In a nutshell
With such a wide range of use cases, sentiment analysis can offer a multitude of matured technical solutions and machine learning algorithms (or Transformers) as cornerstone.
The boom of cloud technologies offered by Azure, GCP, and AWS have also provided opportunities to quickly implement such solutions across all use cases with minimal investment.
As the pioneer of enterprise level sentiment monitoring solutions, Credera UK holds significant expertise in this field and has a proven track record of helping clients to get the best out of these cutting-edge techniques.