Many brands thrive on mentions on social media posts. It is not rare to see a spike in business bottom lines post a robust and sustained social media buzz around the brand. Hence, companies monitor every mention about their brand and track all types of mentions. If it is a positive mention of the brand, then it definitely helps boost visibility and buzz around the brand. However, it is a totally different story if it is a negative mention.
And how do you differentiate between the two?
Carry out sentiment analysis.
The underlying principle of the technique aligns with the basics of the word ‘sentiment’. It is a synonym for feeling, emotion, or attitude. Sentiment analysis works at two levels –
1 – Gathering and assessing opinions and comments of individual and groups
2 – Apply a logical scoring mechanism to quantify the attitudes and opinions
Also referred to as sentiment mining or opinion mining, the idea is to track all social media platforms and continuously monitor who is saying what about a brand.
But why social media?
Let’s face it. Social media has become one the biggest decision influencers online in recent times. As per Hubspot, 92% of the marketing professionals have corroborated to the immense importance of social media to their business. Also more than 50% of the marketers who have been using social media for at least two years have mentioned that it has aided in enhancing sales. Looking at the tremendous traction that can be driven by social media, businesses are embracing social media like never before.
The catch is, the social media has billions of people with the power to voice their opinions and thoughts on a public platform. Now what if some minor issue snowballs into a social media catastrophe with devastating business implications? This is exactly the kind of situation sentiment analysis seeks to avoid.
Sentiment analysis is a way of measuring the emotions that drive social media mentions around the brand. It tracks the tone behind a conversation or a post – is the person happy, satisfied, sarcastic, or angry?
Machine learning plays a vital role in sentiment analysis for businesses who take their social media presence seriously. It uses classification algorithms like Naïve-Bayes and Support Vector Machines (SVM). With this, a classification is executed on a set of social media comments. This classification is based on a pre-defined list of words marked as positive or negative by the machine learning system.
Now, it cross checks the words in the comment with this pre-defined list, and gives a score of +1 (for positive words) and -1 (for negative words). It may drill deeper and give more weightage to higher degree of negative or positive words. If the overall score of a comment is positive, it is marked as a positive comment.
An important aspect of the accuracy of the algorithm is the ability to recognize the context behind a comment. Consider this Twitter post of an irate air traveler –
“My flight’s delayed. Brilliant! ”
Now, we humans will understand that the word ‘brilliant’ has a sarcastic overture. We can also associate with the frustration of the passenger for having to wait for the flight to take off. By applying the contextual understanding of this tweet, we can recognize the negative sentiment behind this post.
It is mission-critical for businesses to listen carefully to feedback about their brands. More importantly, it is crucial to gauge whether the comment or mention is an asset or a liability to a business. Sentiment analysis helps to assess people’s opinions on services, products, topics, companies, or even the leaders behind the brand. Here are 4 compelling reasons why sentiment analysis matters for your business –
You can tailor your marketing and outreach programmes based on the reactions you encounter for a particular line of product. The interesting thing about posts is that it snowballs rapidly with massive sharing and likes if the sentiment is negative. Hence sentiment analysis helps avoid such situations and lets you take mitigating measures to reduce the negative impact.
Sentiment analysis is widely used by customer support representatives as they can respond promptly and more efficiently to building dissatisfaction before the negative sentiment hurts your business. This is especially true for B2C ventures like hotels and restaurants. Every negative comment will be viewed by others and hence it is important to respond to it and display empathy or suggest measures to reduce the dissatisfaction. With such detailed monitoring, others too see how well you respond on time to satisfy an irate customer, thus winning you more possible business.
By having better customer support, marketing campaigns tailored to the sentiments prevalent on public platforms, your sales team can have a better pulse on the market. This in turn, works well to let you generate leads by letting you know exactly what the customers are talking about. An additional plus point is that loyal customers happy with your accessibility, courtesy, and promptness on their social media handles lead to more referrals, thus boosting your lead generation potential.
With sentiment analysis, you can manage brewing social media problems and avert catastrophic PR scandals. The most common example of crisis on social media stems for B2C facing sectors like restaurants. It is not rare to see restaurants getting slammed for multiple reasons – food quality, service quality, or ambiance. The good ones take their social media presence seriously and make sure to discover disgruntled customers or employees. Only when you know about their existence, will you be able to remedy their complaints. And this is what sentiment analysis does – it lets you get in touch with disappointed customers and respond to them before it becomes viral in a negative way.
Sentiment analysis continues to find useful practical applications in various sectors like PR, politics, businesses, and individual brand building. With this, clients are demanding more value from this niche. Here are 4 key challenges that sentiment analysis experts feel will be prominent to this field –
It is hard to give accuracy to your specific analysis. There are many moving parts such as the mention being tracked, the level of text being analyzed, the amount of data available for analysis, and the quality of the video channel being used for the analysis. The good news is that with continuous tech evolution, the quality of resources and tools used for this subject is improving every passing day (refer below to our list of top tools for sentiment analysis).
A blended approach is needed to get the right mix of human context with computational prowess of machines. The prior experience of human beings comes in handy when analyzing a specific mention to go beyond superficial knowledge. This will add more muscle to your sentiment analysis and provide a more accurate picture of the mood of the customer.
Most of the customers approach sentiment analysis with a fixed set of theories and hypotheses. With these in mind, they extract and measure only a limited part of social media mentions that aligns to their theories. An ideal approach will be to have a universal monitoring of brand sentiment and then analyze the data in its totality. This will help uncover insights that can go beyond what we expected and give a factual representation of the customer opinions going around.
It will be unwise to go on a sentiment analysis expedition once a while. This gap in tracking is a key reason why sentiment analysis has not been able to emerge as a mature business function for many smaller companies. For serious brands, sentiment analysis is an on-going activity and needs non-stop serious commitment.
Sentiment analysis has come to be known as one of the most reliable tools to listen effectively to social media chatter around your brand. It helps you to know what people are saying about your brand, gather collective opinions, and tells you if you need to be taking any action to keep up the positive brand perception.