Sentiment analysis is an evolving field of study focusing on applying artificial intelligence (AI), natural language processing (NLP), data mining, and machine learning (ML) to gain a deeper understanding of the sentiment behind text.
The most common use case is helping companies understand how customers or clients feel about products or services. However, sentiment analysis can be applied more broadly in any situation where the sentiment of a large amount of unstructured data must be understood.
Customer sentiment analysis allows companies to consistently interpret the sentiment behind reviews, support inquiries, and social media posts without relying on manually reading each one. So, let’s explore how this growing field is already proving itself to be valuable to organizations in nearly every industry.
Like other fields of AI, sentiment analysis is an overall term that is applied in several different ways. Some of the most common types of sentiment analysis are:
Understanding each of these types of sentiment analysis and when to use each one is crucial to getting the most value from them.
Why should organizations invest in sentiment analysis tools and capabilities? Some of the overall benefits might already be apparent, but let’s drill down into exactly how sentiment analysis can help your business.
Monitoring brand mentions on social media is an established practice, but it quickly becomes challenging as an organization grows. Having a team to address specific issues or questions is valuable, but what about posts that do not require a response?
A purpose-built sentiment analysis tool can monitor social media mentions and evaluate the intent, mood, or aspect of your company being discussed. The tool can then provide an overall summary of how users are talking about your company, rather than relying on manually going through each post to try to understand customer sentiment.
Some sentiment analysis tools have limitations on how they can be integrated and what data they can work with. However, as this field continues to be developed and explored, these limitations are becoming more rare. Now, there’s a growing need and potential for integrated analysis with other tools or systems.
For example, let’s say you have a virtual environment set up for customer training. Integrated tools can provide customer training analysis to evaluate the effectiveness and attitude towards the training.
As development continues, we expect to see more robust and beneficial integrations to help organizations better understand their customers and clients in entirely new ways.
Understanding the sentiment in real time can open up entirely new use cases throughout the organization. This capability will require a large amount of training data and available resources to process text or speech rapidly. However, once deployed, it can provide entirely new utility for employees to put to use.
For example, understanding if a lead has the intent to buy or walk away during a sales call can help sales representatives adjust their approach as necessary. This same feature can be used for customer support representatives to help ensure a positive customer experience.
Having a detailed understanding of how customers and the general public view your company is highly valuable for any PR or marketing strategies. Being aware of your public perception has always been important, but sentiment analysis can now unlock an entirely new level of insights to inform your campaigns.
Are customers viewing your company or specific elements of a new product negatively? PR campaigns can speak to this by addressing it head-on.
Similarly, if customers praise a specific feature, marketing campaigns can capitalize on this perception by highlighting that feature in future campaigns.
Having deeper, data-driven insights into how potential and future customers view your company can go far in crafting more effective campaigns, whether driving sales or building a positive reputation.
On top of how time-intensive manual analysis can become, it’s also a subjective practice when handled by employees. Two employees might consider the same review positive or negative, which can significantly skew overall results.
Sentiment analysis tools bypass this issue by applying the same method of analysis to every social media post, support inquiry, or review. You’ll be able to confidently take action on the analysis since everything was evaluated in the same way.
However, these tools aren’t error-free. For example, users who use colloquial language may still be challenged. Fortunately, these tools will continue to improve to better understand different ways of speaking and maintain consistent results.