Today, businesses have access to numerous data of their customers. It can be in the form of customer reviews, survey results, social media posts, and chats. But how can you use this data to create meaningful insights for your business?
It can be easily done through sentiment analysis.
Remember, every customer has a different experience with your product or service. You can use the old-fashioned method of writing all these notes in your notepad. But it won’t bring any fruitful results from this strategy.
That’s why you must follow a strategic approach to understand your customers. Using sentiment analysis lets you clearly understand how your customer perceives your business.
But how can you start sentiment analysis? What are its types and algorithms? In this blog, we will discuss everything in detail so you can get started with this strategy.
What is the Sentiment Analysis?
Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique that involves the automated analysis of text data to determine the sentiment or emotional tone expressed within it.
In simpler terms, sentiment analysis aims to understand whether a text expresses positive, negative, neutral, or even more nuanced sentiments.
Sentiment refers to positive or negative statements expressed in the text. Through this, you can evaluate and analyze written or spoken language and then determine whether it’s:
- Favorable
- Unfavorable
- Neutral
- Or what’s the context of the text
You can use this information to understand customer sentiment. Let’s understand it better with this example.
You might have left an online review or comment about a brand. It can be about their services, online store, or any product in general. There is a high chance that your comment has been through sentiment analysis.
Sentiment analysis falls under the broader text mining category, often called text analysis. The process involves assigning a numerical score to each text segment based on sentiment. For instance,
- A score of -1 may indicate negative sentiment.
- A score of +1 signifies positive sentiment.
These assessments are made possible by applying natural language processing (NLP) techniques. With this approach, you can get a wide range of insights regarding your target audience.
Types of Sentiment Analysis
You must learn about this strategy for your business’s basic types. Let’s look at the major types of sentiment analysis.
Fine-grained Analysis
It includes the identification of the polarity of the opinion. It’s not complex at all. It’s as simple as referring to the text as positive or negative.
However, this approach can also be extended to a more refined scale. Do you know how?
By broadening the categories such as:
- Very positive
- Positive
- Neutral
- Negative
- Very negative
Let’s understand it with an example. A new movie is launched, and there are two sorts
of reviews.Review 1: This movie was fantastic! I loved every minute of it.
Fine-grained approach: +1 (positive)
Review 2: This movie was an absolute masterpiece. The acting was phenomenal, and the plot was brilliantly crafted.
Fine-grained sentiment analysis: very positive
That’s how you can understand any product reviews better.
Emotion-based Analysis
Emotions are the fuel to connect with your target audience. It focuses on detecting specific emotions expressed in text, such as joy, anger, sadness, or surprise.
Do you know how these are determined?
By using lexicons and machine learning algorithms. They determine what is, why, and how people are feeling. In short, you’ll get the emotional tone of your content.
A good example of this is determining the emotional reactions of social media users to a recent news event.
Aspect-based Analysis
It is also referred to as feature-based sentiment analysis. The focus is to identify the bias of customers related to specific product features or modifications mentioned in the text. The aim is to determine how different aspects are perceived.
For example, analyzing customer reviews of a smartphone to assess opinions of customers about its battery life, camera, and design separately. It’s to get product analytics and keep an eye on how your customers perceive your product. Not only this, it includes strengths, weaknesses, and improvements in your product to create a product that wins your customer’s hearts.
Intent Analysis
It’s all about the action. It’s not purely based on analyzing sentiments. It is most commonly used in customer support systems. It helps streamline the workflow because they use it to identify whether customers are seeking assistance, expressing frustration, or providing positive feedback.
How Does Sentiment Analysis Work?
There are two major things involved in this approach. It helps in the identification of text, whether it’s positive, negative, or neutral. The two major algorithms you should know are as follows.
Rule-based Approach
This approach is used on an algorithm with a clearly defined opinion description to identify. It includes the identification of things such as:
- Subjectivity
- Polarity
- The subject of an opinion
There is a rule-based Natural Language Processing routine. It includes operations such as stemming, tokenization lexicon analysis, and parts of speech tagging.
Here’s how it works:
There are two major lists of words. All the positive terms are included in one list, while the other list will include negative ones only.
Now, the algorithm will check all the texts and find the words that match the criteria you’ve already mentioned. Now, the algorithm has all the data. It’ll analyze which types of terms are most frequently used in the text.
Positive words = Positive polarity
However, rule-based sentiment analysis is not 100% precise and lacks flexibility. Due to this, it’s not completely usable. Which means it will not take context into account. So what should you do? How can you use this approach?
It is used for general purposes, such as finding the tone of text, which is an important task in customer support. It’s commonly used to establish the foundation upon which machine learning solutions are subsequently developed and trained.
Automatic Sentiment Analysis
The rule-based approach is useful, but if you’re planning to get real market insights more accurately, automatic sentiment analysis is the real deal. This approach digs deeper into your content and delivers the best.
Rule-based uses → Clearly defined rules.
Automatic analysis uses → Machine learning.
As a result, the precision is much higher in automatic sentiment analysis. You can analyze large amounts of data without encountering too many issues.
It uses computational techniques to analyze text data. It involves data collection, preprocessing, feature extraction, model selection, training, and evaluation.
The model predicts sentiment scores or labels (e.g., positive or negative) for new text data and can be fine-tuned over time. The success depends on data quality and the choice of machine learning model.
Overall, there might be other types of algorithms classified into this.
Linear Regression
It is a statistical method used for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. It’s often used for predicting numerical outcomes.
Naive Bayes
Naive Bayes is a probabilistic machine learning algorithm based on Bayes’ theorem. Given the class, it’s primarily used for classification tasks and assumes that features are independent.
Support Vector Machines (SVM)
These are a type of supervised machine learning algorithm used for classification and regression tasks. SVM finds an optimal hyperplane that best separates data points into different classes. It maximizes the margin between classes.
Benefits Of Sentiment Analysis
There are various benefits to implementing this innovative approach. Here are some of the top benefits you should know as it offers a range of benefits across various industries and applications.
- Product Development: Businesses can identify product strengths and weaknesses by analyzing customer feedback. This data informs product improvements and innovation.
- Market Research: It provides valuable insights into market trends and consumer preferences. Companies can use this information to make informed decisions and gain a competitive edge.
- Competitor Analysis: Organizations can track insights using this approach. They can identify their competitors, market gaps, and opportunities. This can inform strategies for market positioning and differentiation.
- Risk Management: It is used in risk assessment to monitor and analyze sentiment related to potential risks, crises, or emerging threats to organizations.
- Social Listening: Brands and organizations can monitor social media sentiment to track the impact of marketing campaigns and measure audience engagement.
Conclusion
In sentiment analysis, advanced deep learning and natural language processing are used, which help identify whether the text is positive, sarcastic, negative, or neutral. This strategy is extremely crucial for all sorts of businesses.
If you’re dedicated to providing the best services to your target audience and outshining your competitors, then it’s a suitable approach for your business. Because it helps you get better insights, which can be used as a basis to make new products, make updates in existing products, etc.
However, it’s an ever-changing field. It’s constantly developing and evolving. Keeping up with it can be quite difficult. What should you do in this case?
It’s by taking the help of tools. They save time and increase ROI, which every business wants. Luminoso is a software that can help you do sentiment analysis with ease.
