One of the things that define us as human Is our ability to showcase a wide range of emotions. We can be sad, excited, worried, and angry within seconds. We can learn and analyze situations like no other animal can. These are just some of the things that makes us unique and special.
This is evident during shopping, too. Almost all purchases are emotion-driven. It could be out of fear, jealousy, or happiness. With emotions playing a critical role in customer behavior, it has become essential for brands to analyze the sentiments of their consumers.
Here's where the concept of sentiment analysis comes into play. In this post, we'll discuss the idea and different types of sentiment analysis.
First things first, what is sentiment analysis? Sentiment analysis is a type of market analysis that includes the use of text analysis, biometrics, natural language processing (NLP), and computational linguistics to recognize the state of the said information.
In simple terms, it's the process of determining whether a piece of content – email, social media post, or article – is negative, positive, or neutral. Sentiment analysis enables you to ascertain public opinion and understand consumer experiences.
But why should you even bother about sentiment analysis? For starters, it's extremely helpful in social media monitoring. It helps you gauge public opinion on certain topics on an enormous scale.
Besides, it can play a pivotal role in market research and customer service. With sentiment analysis, you can see what people think about your products or your competitors' products. This helps you understand customer attitudes and preferences, enabling you to make informed decisions.
People have a wide range of emotions – sad or happy, interested or uninterested, and positive or negative. Different sentiment analysis models are available to capture this variety of emotions.
Let's look at the most important types of sentiment analysis.
This sentiment analysis model helps you derive polarity precision. You can conduct a sentiment analysis across the following polarity categories: very positive, positive, neutral, negative, or very negative. Fine-grained sentiment analysis is helpful for the study of reviews and ratings.
For a rating scale from 1 to 5, you can consider 1 as very negative and five as very positive. For a scale from 1 to 10, you can consider 1-2 as very negative and 9-10 as very positive.
While fine-grained analysis helps you determine the overall polarity of your customer reviews, aspect-based analysis delves deeper. It helps you determine the particular aspects people are talking about.
Let's say; you're a mobile phone manufacturer, and you get a customer review stating, "the camera struggles in artificial lighting conditions."
With aspect-based analysis, you can determine that the reviewer has commented on something "negative" about the "camera."
As the name suggests, emotion detection helps you detect emotions. This can include anger, sadness, happiness, frustration, fear, worry, panic, etc. Emotion detection systems typically use lexicons – a collection of words that convey certain emotions. Some advanced classifiers also utilize robust machine learning (ML) algorithms.
It's recommended to use ML over lexicons because people express emotions in a myriad of ways. Take this line, for example: "This product is about to kill me." This line may express feelings of fear and panic.
A similar line – this product is killing it for me – has an entirely different and positive meaning. But the word "kill" might be associated with fear or panic in the lexicon. This may lead to inaccurate emotion detection.
Accurately determining consumer intent can save companies time, money, and effort. So many times, businesses end up chasing consumers that don't plan to buy anytime soon. Accurate intent analysis can resolve this hurdle.
The intent analysis helps you identify the intent of the consumer – whether the customer intends to purchase or is just browsing around.
If the customer is willing to purchase, you can track them and target them with advertisements. If a consumer isn't ready to buy, you can save your time and resources by not advertising to them.
Modern-day sentiment analysis approaches are classified into three categories: knowledge-based, statistical, and hybrid. Here's how to perform sentiment analysis.
Back in the day, performing sentiment analysis required expertise in technologies like Python, R, and machine learning. But nowadays, several software tools enable you to conduct sentiment analysis with no or minimal technical knowledge.
Sentiment analysis is one of the most valuable technologies in today's emotion-driven market. But it's not an easy process, and if done incorrectly, it can lead to inaccurate results. Therefore, it's crucial to work with the right company that offers sentiment analysis solutions.
