Sentiment Analysis – An Ultimate Guide For 2023
You are in business to profit and scale, right? Then, you would surely want to know what your customers feel about your products or services. Both positive and negative feedback matter as they can help grow your business or be triggers of crisis. The basic thing to do here is to know the customers’ perception of your products or services. The good news is that you can measure customer feelings through sentiment analysis.
Customer expectations and their perception of products and services keep changing with time thanks to the changes in the environment, customer needs, the launch of new brands, and technological innovations. For example, the development of e-commerce, SaaS products, and digital technologies is influencing customers’ sentiments, making it necessary for sentiment analysis to adopt those changes in the current times.
We will look at the types, importance, use cases, and methods of sentiment analysis.
What Is Sentiment Analysis?
Let us start with understanding what sentiment analysis is. It is the process of understanding customers’ sentiments through analyzing text data and mining. Sentiment analysis is also known as opinion mining. You can use this approach to recognize and categorize the customer opinions expressed in texts on your products and services.
Benefits of Sentiment Analysis
Sensitive analysis delivers you insights into the customer’s feelings about your products and provides you with actionable suggestions in the following ways:
- Tracking sentiments in real-time: Opinion mining helps you understand the sentiment of existing customers in real time and help you take immediate action.
- Improving products and services: Using this approach, we can analyze customer feedback to help improve the performance of the products and services.
- Actionable data: It provides actionable data from social media and other sources to give insight into your brand image and product performance.
- Strategizing marketing campaigns: It helps you assess and improve your marketing strategy and campaigns with the help of insights.
- Monitoring brand image: It can help you know about the customer perception of your company so that you can take actions to retain or enhance the brand image.
Different Kinds of Sentiment Analysis
You have the following sensitive analysis models to choose from, to capture the sentiments and emotions of your customers and prospects, depending on your company’s needs.
1. Small-Grained Analysis
This method refers to breaking customer reviews and ratings into small phrases and clauses to analyze and make inferences. This allows brands to assess customers’ sentiments by identifying who talks about the products and what they want to convey in their feedback messages. The objective is to define the sentiment intensity by classifying the reviews and ratings as positive, negative, or neutral and, if required, highly positive or negative.
2. Aspect-Based Analysis
Rather than reviewing the product or service, this deeper analysis method examines certain aspects of the product or service. It helps you identify the aspects of business or ideas the customers consider positively or negatively, seeking your attention.
3. Emotion Recognition Analysis
You can adopt this method to define and evaluate the emotions expressed by the customers in textual data. It helps you identify the customer’s emotions, such as fear, rage, anger, satisfaction, frustration, sorrow, happiness, affection, motivation, panic, or neutral. Lexicons and machine learning algorithms enable companies to perform this process.
4. Intent Analysis
You may not want to spend your time on prospects who do not have plans to buy your product or similar products soon. You would rather want to focus on customers who intend to buy your products or your competitors. This model enables you to determine consumer intent. This way, you can plan your strategy accordingly.
Working Models of Sentiment Analysis
Sentiment analysis or opinion mining, also known as emotion AI, leverages artificial intelligence (AI) and machine learning, including natural language processing (NLP), to scan the sentences in the texts to categorize pieces of writing as positive, neutral, or negative. Sentiment analysis might use various types of algorithms.
The following models are currently adopted in sentiment analysis:
1. Automatic / Machine Learning Sentiment Analysis
This analysis method leverages ML and NLP to learn from historical data and accurately analyze the customer’s feelings and emotions. This algorithm can process and analyze large volumes of data, much more than the rule-based algorithm.
2. Rule-Based Sentiment Analysis
This conventional method of sensitive analysis uses preset algorithm rules for identifying and understanding the emotions from any sentence. It works with the help of a library of positive and negative words, tokenization (splitting) the texts into the smallest possible words, grouping similar words in one group, and calculating the sentiment score. Rules can be set around other aspects of the text, for example, part of speech, syntax, and more.
3. Hybrid Sentiment Analysis
This combines both the methods mentioned above and is the most effective solution, delivering high accuracy provided by machine learning and stability from the rule-based, lexicon-based approach.
Sentiment analysis, which keeps changing with the changes in human behavior, society, the way of life, technology, and markets, is essential for marketers, market research, and user researchers. It helps them understand the consumer’s feelings and emotions to get insights into how it will impact your business. Companies can design their marketing plan by knowing the positive or negative impressions of their potential and existing customers. However, doing this exercise manually is not practical. You will need tools powered by the latest behavioral AI technologies to help you achieve quick, real-time, and accurate results.
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