In the age of information, businesses both large and small are using data and metrics to gauge engagement, gain insights and track conversions. However, social media metrics seem to lack meaningful context. They tell you the “what” but not the “why” or the “how”, as they provide primarily quantitative metrics on interactions.
With the use of social sentiment analysis, a business can conduct a contextual analysis of the text that appears in a social media post and extract information which can help them understand how a customer feels. This is done by analyzing the online “sentiment” that each customer includes in his/her posts which can be derived from the tone or emotion connected to a brand or business mentioned.
Sentiment analysis has been made possible through advances in the field of deep learning algorithms which makes it possible decipher and analyze text better than ever before.
Going forward, a business will be able to classify online customer conversions based on two things:
- The Key aspects of a respective brand/business that customers care about
- The customers’ reactions to these key brand aspects and the basic intentions behind them
The analysis of these two factors will help businesses and brands make sense of millions of digital and social conversions.
There are 3 key stages that act as building blocks towards achieving the perfect sentiment analysis.
Stage 1: Basic Sentiment Analysis
At this stage, a business/brand will use the most basic analytical tool to analyze in-coming messages, posts and comments to find out if a customer’s sentiments are positive, negative or neutral.
Stage 2: Intent Analysis
At the next stage, a business/ brand will analyze the intent behind a customer’s message and identify if it fits within one of the following categories.
- Is it an opinion?
- Is it a piece of news?
- Is it a marketing query?
- Is it a complaint?
- Is it a suggestion?
- Is it a show of appreciation?
- Is it a general query?
Stage 3: Contextual Semantic Search (CSS)
At this stage, after analyzing the basic sentiment of the post and examining the intent behind the post, the business must attempt to derive actionable insights; this could be, for example, through isolating the different pain points that customers are expressing. Consider a food delivery business where the pain points may be late deliveries, billing issues and the quality of the food delivered etc. An algorithm would pull queries related to these pain points from the combined input of messages and allow the business to find out how many of its customers are complaining about each of the pain points specifically; thereby allowing businesses to prioritize their improvement efforts.
There are many ways this analytical process could benefit a business. It could be used as a tool to prioritize social media engagement by ensuring that you deal with the most important queries and messages first before you move on to those with a lesser level of importance as we saw in the previous example. Sentiment analysis is also a good tool to measure your brand’s digital reputation. It allows you to gauge the feelings your audience has towards your brand and which aspects appeal more to your audience than others. This would help in future marketing campaigns, for new product development and in formulating promotions and special discounts. You could also measure your reputation over time to see long term trends.
You could also use sentiment analysis to preempt and manage crises that may arise out of a bad review or comment that may appear online. Analyze the sentiment behind the comment, reach out to the person who made it, and potentially avoid toxic publicity to your brand.
Sentiment analysis is a tool that will help give context to the messages and conversations that concern your brand/ business and allows for growth based on data and meaningful metrics.