Large enterprises and small business owners alike are now increasingly aware of how social media has the power to make or break their brands. Brands are careful and conscious about their social media presence as it is now a major touch point for customers to interact with the brand. With this in mind, many organizations are thinking about running a sentiment analysis project to better understand what their customers and target audience are saying and thinking about their brand.
What is sentiment analysis
Sentiment analysis is an automated process which uses NLP, computational linguistics and text analysis to identify, extract and analyze a unit of data in written text or speech to discern whether the opinion is positive, negative or neutral. It is also known as opinion mining or emotion AI.
Why you should use sentiment analysis
Most available data is unorganized in the form of emails, customer support chats, social media, surveys, news articles, blogs and other online documents. Going through this manually is time-consuming and expensive. Sentiment analysis allows companies to sift through this pile of data to achieve insights that enable more informed decisions. With machine learning, sentiment analysis becomes more accurate over time.
Most companies are already measuring some of the online chatter about their brand. But it is important to remember that more mentions on Facebook Instagram or Twitter don’t necessarily mean that customers are happy with the brand. Sentiment analysis can analyze the vast troves of chatter about the brand and discern if it is positive or negative, thereby providing deeper insights that can help companies know what they should act upon.
Types of Sentiment Analysis
Sentiment analysis can be applied at the document level, the sentence and even sub-sentence level to gauge the sentiment of opinions. Coarse-grained sentiment analysis provides the basic level of polarity of opinion on a particular topic or keyword.
Fine-grained sentiment analysis offers a more precise level of polarity of opinions. For example, it can tell you if an opinion is very positive, positive, neutral, negative or very negative.
Emotion detection systems use ML algorithms to detect emotions like sadness, anger and joy in the sentences. But this can be limiting as people can use words or slang to mean something other than what is expressed. To go further, intent analysis can be used to decipher what people intend to do when they say or use a particular text or sentence.
Additionally, aspect-based sentiment analysis can distinguish what persons are saying about an aspect or feature of the product that they are talking about and further demarcate it as positive, negative or neutral.
Marketing campaigns for big brands now factor in working with influencers on social media to help with promotions. But identifying the ‘right’ kind of influencer can be tricky.
With the use of sentiment analysis, marketing managers can not only identify the influencers that best the brand and use it to vet the influencers to verify that they can positively promote the brand. This is key to avoiding any potential PR disasters that may rise and spread quickly in social media. For example, marketing managers can set up trackers around certain subjects and keywords on social media to see which influencers are actively participating in the topic conversation and the kind of sentiment that they evoke with their posts and tweets.
They can also set trackers on the influencer’s social media accounts to see over time how their brand features in the influencer’s conversation or news feed and what followers feel about the same.
Sentiment analysis can help marketing teams better assess the impact of their campaigns. Marketing managers can monitor the number of mentions of their brand before and after a campaign to estimate how the campaign has resonated with the target audience. Through the long term, it can help identify trends within the industry that marketing teams can use to their advantage while planning campaigns. More importantly, with integration of other data, it can create a visual snapshot of how social data and sales data are connected, which will enable C-suite to see clearly the impact of the marketing campaign.
Advanced Sentiment Analysis can use the form data aggregation and quantification to provide a big-picture view to measure ROI via a dashboard with interactive graphs and charts. The data can be further examined and diced for further analysis. The sentiment evolution of a brand over time can be tracked through a sentiment timeline.
Business Use Cases
Companies can use sentiment analysis to decipher the quality of the conversation around their brand. After analysis, it can automatically categorize the urgency of all online mentions of the brand and alert team members concerned, which can help in avoiding major public relations disasters.
Sentiment analysis helps companies refine their products. After building an MVP, the product team can get valuable feedback from the target audience. Sentiment analysis can help reveal what customers and users like or dislike about the product and can help the teams decide which features can stay, go or improve.
Large customer-facing organizations and institutions can rely on sentiment classification to optimize their customer care workflow. Customer support messages are classified and analyzed according to the topic priority and polarity. They are then sent to the specified team members for action.
Companies can use sentiment analysis on their vast amount of unorganized data to understand their customers better. It allows them to build better products and be more responsive to customer needs. Ultimately, sentiment analysis can help companies build and maintain their brand.