Sentiment Analysis in Brand Monitoring
‘Sentiment analysis’ or opinion mining is an often misunderstood and bandied term. Today sentiment analysis is more of a business trend that empowers organizations to gauge customer intentions to better understand their brand image, products, and services by analyzing the attitudes, opinions, and emotions expressed by an online audience. Sentiment analysis has gained immense popularity in the business world owing to its applicability in a range of processes and it’s the ability to unearth collaborative recommendations and summarize feedbacks from the product reviews.
Sentiment analysis not only helps to understand the voice of customers by providing detailed insights into the nuances of customer experience but also provides answers to why and how the shifts occur over time.
The client- a leading private label goods manufacturer in the United Kingdom. Apart from being one of the leading private label companies in the world, the client owns more than 30 of its own private label brands.
With several private label brands developing unique strategies to establish their brand presence and set their mark in the global landscape, the client found it extremely difficult to identify customer needs. However, with the growing popularity of social media, private label companies are now looking at leveraging the immense potential in translating social data into tangible insights.
The private label brand wanted to leverage sentiment analysis to monitor the customer’s sentiments with regards to their products and the brand as a whole. The main aim of the client was to leverage sentiment analysis in social media monitoring such as twitter sentiment analysis to identify potential influencers across social media platforms.
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A detailed analysis of social media discussions and online reviews helped the private label brand to assess customer perceptions of their brand. Quantzig’s expertise in sentiment analysis helped them leverage statistical modeling techniques to keep track of real-time customer sentiments. The development of accurate sentiment classifiers made it easier to identify the tone of customer sentiments.
Over the course of a month, we collected and analyzed millions of tweets and other social posts mentioning the private label brand, posted by customers across the globe. Twitter sentiment analysis proved to be valuable in classifying the gathered tweets with a particular sentiment of either neutral, positive, or negative.
The recommended approach to sentiment analysis outperformed the accuracy of the previous approach by over 70%. By employing sentiment analysis, the private label brand was able to successfully classify customer sentiments into different categories such as- positive, negative, and neutral. Moreover, the approach to sentiment analysis turned out to be highly accurate and reliable, delivering accurate results on customer perceptions. With such accurate predictions, private label brands will be better positioned to predict customer needs and develop strategic plans to meet them.