How You Can Get The Most Out Of Sentiment Analysis
Tracking and analyzing sentiments has emerged on the scene alongside countless other automation processes in the last decade. Sentiment analysis has been popular with social media and discovering how people talk about brands and products—also called "social listening." However, sentiment analysis has many more use cases outside social media that aren't as often associated with the process.
Sentiment analysis uses machine learning techniques like natural language processing (NLP) and other calculations such as biometrics to determine if specific data is positive, negative or neutral. The goal of sentiment analysis is to help departments attach metrics and measurable statistics to pieces of data so they can leverage the sentiment in their everyday roles and responsibilities.
This article will explore the uses of sentiment analysis, how proper sentiment analysis is achieved and why companies should explore its use across various business areas.
Use Cases For Sentiment Analysis
Outside of social media, there are many places where people are talking about your product and services. These include phone logs, chatbot communication, review websites, support tickets, articles, internal documents, emails and more.
With the help of artificial intelligence, text and human language from all these channels can be combined to provide real-time insights into various aspects of your business. These insights can lead to more knowledgeable workers and the ability to address specific situations more effectively.
For example, in customer support, by connecting fields of text from chat logs, phone calls, emails and support tickets, customer service representatives can prioritize their tasks and the order in which they assist companies or individuals. This applies to B2B and B2C customer service.
Many large companies are overwhelmed by the number of requests with varied topics. NLP and natural language understanding (NLU) can detect the emotion and tone behind the written or spoken word, helping companies understand the urgency of specific requests and support tickets. Classification also plays a role in sentiment analysis and can be used to sort requests to the proper channels or departments.
Like customer support and understanding urgency, project managers can use sentiment analysis to help shape their agendas. In addition to classifying urgency, analyzing sentiments can provide project managers with assessments of data related to a project that they normally could only get manually by surveying other parties. Sentiment analysis can show managers how a project is perceived, how workers feel about their role in the project and employees' thoughts on the communication within a project. Feedback provided by these tools is unbiased because sentiment analysis directly analyzes words frequently used to express positivity or negativity. Project managers can then continuously adjust how they communicate and steer the project by leveraging the numeric values assigned to different processes.
Sentiment analysis is also a way to listen to your employees. Sentiments from hiring websites like Glassdoor, email communication and internal messaging platforms can provide companies with insights that reduce turnover and keep employees happy, engaged and productive. Sentiment analysis can highlight what works and doesn't work for your workforce.
How Proper Sentiment Analysis Is Achieved
Entirely staying in the know about your brand doesn't happen overnight, and business leaders need to take steps before achieving proper sentiment analysis.
The first step involves choosing your content. What exactly are you looking to measure? Is it online reviews or email correspondence to gauge employee satisfaction? Identifying the business need as precisely as possible is essential before gathering your datasets and training the machine learning model.
The next step is gathering your data. Luckily, gathering and labeling data is a process that can now be automated. Manual data labeling takes a lot of unnecessary time and effort away from employees and requires a unique skill set. With that said, companies can now begin to explore solutions that sort and label all the relevant data points within their systems to create a training dataset.
Finally, companies can begin the machine learning process. This process requires training a machine learning model and validating, deploying and monitoring performance. Using the training dataset from the previous step with NLP and NLU, content is classified as "positive," "negative" or "neutral." After fine-tuning the model for your required sentiment analysis task, it's time to validate and deploy the model for real-world business use.
Next, monitor performance and check if you're getting the analytics you need to enhance your process. Once a training set goes live with actual documents and content files, businesses may realize they need to retrain their model or add additional data points for the model to learn.
Why Companies Should Explore Sentiment Analysis
If working correctly, the metrics provided by sentiment analysis will help lead to sound decision making and uncovering meaning companies had never related to their processes.
Companies focusing only on their current bottom line—not what people feel or say—will likely have trouble creating a long-existing sustainable brand that customers and employees love. Sentiment analysis can help most companies make a noticeable difference in marketing efforts, customer support, employee retention, product development and more.