Manufacturers Turn to Sentiment Analysis to Leverage Social Media

Tesla’s Elon Musk is leading the way on how auto manufacturers can deal with customer complaints using social media incredibly rapidly and effectively.  At the same time, Elon is also showing how not to handle social media.

It’s clear that social media has become a critical data source for feedback from customers to manufacturers. Social media is critical for not just improving customer support, but it’s important for all aspects of a manufacturer’s business – from product planning to marketing campaigns, reputation management, and competitive analysis. It can make the difference between innovation and success or stagnation and failure. How can manufacturers integrate social media into their business strategies?

Handling and fully leveraging social media takes much more than responding to a handful of tweets, though.  Twitter feeds are so large that human effort alone is not capable of going through and analyzing them in a timely and cost-effective manner.  Here is where a new AI-based technology known as Sentiment Analysis has truly revolutionized the way in which manufacturers can gauge the opinions and reactions of their customers to their brand, their product features, and how well they provide customer service.

Sentiment Analysis: the Key to a Better Understanding of Customers

Sentiment Analysis, also known as opinion mining, is about detecting likes, dislikes, emotions, and opinions and is often applied to social media, reviews, blogs, forum posts, chats, and similar sources.  Sentiment Analysis is especially well suited for mining online content to extract insights into customer emotions and preferences. Here’s how.

Sentiment Analysis measures Buzz. At the very basic level, it identifies how much a manufacturer’s brand or product is being discussed.  Using Sentiment Analysis, companies can monitor the number of mentions of their products, brand names, promotional hashtags, or slogans over time. A high level of buzz could be good, but it could also be bad, as when a product ad becomes controversial or there is a major piece of negative news about a company. (Boeing is getting a high level of buzz on social media right now, which has dropped its stock market value substantially, but it’s likely not too happy about that.) Finally, comparing the buzz of your own brands with those of your competitors can give you insight into how effective you are in your overall target markets.

Sentiment Analysis identifies Likes and Dislikes. It detects positive and negative sentiment around a brand or product. At a very basic level, it identifies positive and negative language. At a more advanced level, through Entity- and Aspect-based Sentiment Analysis, it pinpoints the specific aspects that those positive and negative sentiments are about. For instance, customers may love the latest product release, but be greatly disappointed by poor customer service.

Sentiment Analysis identifies Intent. It can detect language suggesting intent to purchase a product or alternatively to boycott it. It also detects both positive and negative recommendations.

Sentiment Analysis works on large amounts of data. It scales to Big Data size, making it possible to process massive amounts of data in real time. Naturally, the larger the amount of data and the faster it can be processed, the more reliable, useful, and timely the sentiment analysis will be.

Sentiment Analysis enables Analytics. It normalizes the extracted information (e.g., “I love X” and “I adore X” are represented the same way in the structured output). This enables data aggregation that will provide an overall view of the sentiment expressed in a large collection of content. Sentiment analysis also requires a good visual display: one popular way is to use a dashboard that presents multiple views of the sentiment information through various types of interactive graphs and charts. Using a dashboard, a manufacturer can monitor how its new product release is trending on social media. The user can also drill down to the source tweet or other text for further analysis.

In summary, in today’s fast changing consumer environment, where there are so many market pressures and competition is fierce, manufacturers turn to online content for insights into consumer and market trends. Sentiment Analysis is the AI-based technology for the modern age. It helps manufacturers perform more cost-effective and accurate market research, brand and product reputation monitoring, customer service, and competitive analysis.