Every customer establishes a certain brand image based on the services provided. This image typically takes the form of customer feedback that can be measured to fetch valuable insights. But how can you measure customer satisfaction, emotions, and intent based on tweets or comments?
Try sentiment analysis. This AI-powered discipline goes hand in hand with the development of e-commerce, SaaS tools, and digital technologies and is widely popular among tech-savvy brands.
With that said, let’s unravel what sentiment analysis is and how you can leverage it to your advantage.
Under The Hood of Sentiment Analysis
Let’s start our journey with sentiment definition. Sentiment analysis is a NLP technique that identifies the emotional tone of the text, whether it’s positive, negative, or neutral.
Companies also utilize it when monitoring digital textual data to help companies identify brand and product sentiment in customer reviews, and fine-tune to their needs. It also may go under the names of opinion mining or emotion AI.
Why Is Sentiment Analysis Important?
In today’s digital balloon, we are pumping out unbelievable quantities of data. But it doesn’t mean we can glean the value from these insights. Brands tend to pile up customer reviews and feedback just to be lost in the jungles of raw information. And even if we attempted to analyze this data, it’d take hundreds of years and another hundred mistakes to analyze it manually.
As a result, a growing number of companies are experiencing an insights void. In this case, they know exactly what insights they need to underpin their decision-making, but have no idea of how to get them.
And this is when opinion mining comes on stage. Therefore, instead of going with their gut feeling, companies automate sentiment analysis and base their decisions on real-world data.
In general, companies can tap into the minds of customers using limitless sources of both public and private information. These include but are not limited to:
- Customer service correspondence linked with your services;
- Review platforms;
- Expert product reviews in the media;
- Social Media presence, etc.
Types Of Sentiment Analysis
Emotion AI can target various flavors of emotions. Thus, you can apply AI algorithms to identify the polarity of emotions (positive, negative, or neutral). At the same time, opinion mining is also suitable for determining the full spectrum of feelings and emotions as well as identifying the urgency and intentions behind the feedback.
However, there is no ‘one-size-fits-all’ type of sentiment analysis capable of the range this wide. So let’s have a look at various kinds of opinion mining to decide which one you need for your business targets.
Fine-grained Sentiment Analysis
Lots of people tend to perceive fine-grained opinion mining from the binary standpoint. But along with a normal binary sentiment classifier that determines the polarity of the opinion, this type can also deliver more precise results. Thus, it can label the content as very positive, positive, neutral, negative, or very negative. Let’s take hotel reviews as an example:
Very Positive = 5 stars
Very Negative = 1 star
As the name suggests, this kind of opinion mining helps classify reviews based on emotions. Typically, AI algorithms analyze data relying on lexicons. Sometimes, specialists also apply sophisticated ML algorithms. The latter option is more preferable since lots of words are polysemic.
Aspect-based Emotion AI
You can benefit from this category when you want to narrow down your research. It allows you to study the feedback related to particular parts of functions of your product, whether it’s a phone screen or definition.
Product analytics is one of those fields where aspect-based analysis has found wide application. Brands apply it when they need to grasp the product response and identify weaknesses and fortes of their product.
The last type of emotion AI goes beyond emotion labeling. Instead of analyzing the underlying sentiment, it reflects the user’s intention behind the message. Thus, you can use it to classify the content as an opinion, a query, a marketing note, a complaint, and so on.
How Emotion AI Works
At the heart of opinion mining lies a classification algorithm that classifies customers’ opinions.
Therefore, this technology can be leveraged to perform the following activities:
- Detect and pull out the emotion-colored data from a specific online destination.
- Classify it as positive or negative.
- Define the theme, including a specific subject matter.
- Recognize the type of customer.
Emotion analysis algorithms can be categorized as:
- Document-level – for a whole piece of text.
- Sentence-level – performs analysis on the sentence.
- Sub-sentence level – analyzes sub-expressions.
To give you a better vision of the whole process, let’s take a sentence level as an example.
- First, a text is divided into smaller parts such as sentences, phrases, and entities.
- Then, the algorithm determines the subject matter and related words.
- Then, it assigns a sentiment score to each subject matter (-1, +4, 0…)
Sentiment Analysis Algorithms
Rule-based Emotion AI
Typically, this practical approach uses manually set classification rules and emotionally marked vocabularies or lexicons. Thus, it doesn’t apply any training or ML models. These rules usually calculate text class based on emotional keywords and their combined use with other keywords.
However, despite its excellent performance in texts from a particular topic, the rule-based approach is ineffective when it comes to generalization. Rule-based methods are also extremely time-consuming to create, especially when no suitable sentiment vocabulary is available. Also, they don’t take the context into account.
This approach also relies on a simple NLP process. The following operations are common for a this approach:
- Lexicon-based analysis;
- POS tagging.
This is how this approach works:
- The analysis begins with two lists of words. They are all divided into two groups based on their polarity.
- The algorithm then analyzes the text and chooses the data that fits into the established criteria.
- Finally, the algorithm determines the general polarity of the text. If positive ones dominate, then the text is considered to be positive and vice versa.
Among the most popular lexicon-based methods are TextBlob, VADER, SentiWordNet.
Today, the lexicon-based method usually serves as a foundation for further ML approaches.
Automatic Sentiment Analysis
And this is when things get serious. Unlike the lexicon-based approach, automated sentiment analysis leverages ML to automatically extract text features. Therefore, instead of pre-established, rigid rules, this type uses ML to extract the gist of the message.
Usually, this type of opinion mining implements the following algorithms for polarity classification:
- Naive Bayes;
- Decision Tree;
- Logistic Regression;
- SVM algorithm.
In recent years, deep learning methods, which are significantly superior to traditional methods in tone analysis, have also come on stage to reinforce automatic analysis.
The Bottom Line
As a business owner, you can avail of sentiment analysis to find gaps in your marketing strategy, get realistic feedback, and focus on improvement areas. However, this discipline still has a long way to go. Thus, sentiment analysis is rendered ineffective when dealing with irony, tone determination, and context. Yet, this innovative AI tool is second to none for lack of a better.