With big data playing on the front foot in all forms of business operations and Artificial Intelligence performing near to all functions it’s asked to, customer service and experience have taken the next step as well in the form of virtual assistants, semantic search, and more. Every brand should prioritize monitoring and evaluating customer sentiment and feedback, but companies have historically struggled to analyze this data (especially at scale) and turn it into useful information. That no longer has to be the case with artificial intelligence.
The modern customer expresses his/ her opinions in a variety of methods at all times, including social networking platforms, blogs, reviews, and comments. Users on social media are ready to express their opinions on almost any topic, and tools that can sift through this data and transform it into relevant metrics have become invaluable to a wide range of applications.
Businesses collect data in various forms to acquire insights from this customer input. To make sense of the latest product and marketing effort, it’s critical to understand what the customer feels. And when there is enough data available, as is usually the case in the current world, sentiment analysis tools provide consumers and corporate entities with an easy way to track public impressions of nearly anything and give meaning to customers’ insights.
What is Sentiment Analysis?
AI has advanced to the point where it can recognize the tone of a comment, which is extremely useful for businesses and organizations looking to expand their client base, enhance consumer participation, and even identify top influencers. Tweets, comments, reviews, and other areas where your business is mentioned can all provide insight into customer sentiment. Sentiment analysis is the systematic identification, extraction, quantification, and study of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. It is commonly used by businesses to analyze the voice of the customer materials such as reviews and survey replies, as well as online and social media resources to interpret the social sentiment of its brand, product, or service while monitoring online conversations.
Business Value through Sentiment Analysis
Brands can discern tiny fluctuations in opinion and adjust quickly to suit the changing demands of their audience by regularly monitoring attitudes and views about products, services, and even customer service effectiveness. When it comes to online statistics, the key is to look at how people are talking about your business online. In sentiment analysis, AI can gather information from unstructured data and emotional computing with near 100 percent accuracy when it comes to assessing text-based feedback, such as social media posts, in just one use.
A relative sentiment analysis score can help business decision-makers understand how effective their customer service efforts are, as well as determine how people feel about a company’s products and services. Sentiment analysis elucidates even the most pressing concerns. And as sentiment analysis is automated, AI can help quality analysts and business intelligence teams drill down on customer encounters that need more investigation, rather than detecting human emotions precisely on its own.
Organizations can utilize sentiment analysis technologies for a variety of purposes, including:
- Identifying the target demographics or audience.
- Identifying brand awareness, reputation, and popularity at a certain time or throughout a period of time.
- Following up on the performance of marketing campaigns.
- Obtaining input from customers via social media, websites, or online forms.
- Improving customer support.
How does AI work with Sentiment Analysis?
Sentiment analysis can be fully automated, entirely based on human analysis (rule-based approach), or a hybrid of both. In certain circumstances, sentiment analysis is mostly automated with some human intervention, which feeds machine learning and aids in the refinement of algorithms and processes, especially during the early stages of implementation. To assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase, a sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques.
NLP converts human language into a machine-readable format by employing both syntactic and semantic strategies to comprehend the structure of a document. After the text has been analyzed using NLP techniques, it is ready to be classified using ML algorithms. Machine learning allows computers to recognize and predict patterns in data. Sentiment Analysis through an ML-based approach is accomplished by extracting “features” from the text, which are then used to predict a “label.”
To create a sentiment analysis model, one must train a model that classifies text by sentiment given instances of emotions in text. Each of these samples has to be placed in the relevant category. A representative number of samples for each tag to improve the accuracy of your model would be needed. The model learns to associate a given word with a given image after seeing a few samples.
Use Cases of Sentiment Analysis
1. Monitoring the Social Media
Using machine learning tools, businesses can sift through all of the data present in the form of comments, reviews, and polls in minutes, analyzing individual feelings as well as aggregate public sentiment across all social media platforms.
2. Brand Reputation Management
Sentiment analysis can be used for brand monitoring to assess web and social media opinion regarding a product, service, brand, or marketing campaign when it comes to brand reputation management.
3. Customer Support and Chatbots
NLP is used by sentiment analysis technologies to discover phrases, expressions, and emotional tones in the text that indicate urgency. Detecting urgency in customer support tickets can cut response time in half and boost customer satisfaction. The effect of sentiment analysis is used by chatbots to guide the conversation in the proper direction. Chatbots were able to handle 68.9% of chats from start to finish on average in 2019, according to the Comm100.
Also Read: A constructive guide to Conversational AI