How Types of Sentiment Analysis Works with Machine Learning

Suppose you are scrolling your Instagram feed, and you came across a product advertisement that you were recently searching for. Or, have you ever wondered how companies like Netflix or YouTube know exactly what content would best suit your preferences? It’s all possible through the power of sentiment analysis!  

So, in this blog, we will take a look at different types of sentiment analysis methods available today and understand why sentiment analysis is important. We will also discover some of the many exciting ways in which businesses perform them.

What Is Sentiment Analysis?

Sentiment analysis refers to the examination of digital text that uses both ML (Machine Learnings) and NLP (Natural Language process) to identify the subjective information from any text. It helps determine the tone of message, like whether it is negative, positive or neutral. This type of sentiment text analysis is also known as Opinion Mining.  

It is also important to understand various types of sentiment analysis and explore which is the best according to your needs. 

Types of Sentiment Analysis 

There are different types of sentiment analysis techniques that are used to understand and analyze human emotions in various contexts. These are: 

• Aspect-based sentiment analysis.
• Intent-based sentiment analysis.
• Fine-grained sentiment analysis, and
• Emotion detection.

Now, let’s take a closer look at all of them to clearly understand what exactly these types are:-

Aspect-Based Sentiment Analysis 

Aspect-based sentiment analysis is a powerful technique used to understand and evaluate customer opinions about specific aspects or features of a product or service.

For example, if a company sells mobile phones, they can use this type of analysis for any aspect of a smartphone- like screen quality, RAM, Processor, etc. It can help the business to understand how customers feel about the specific features or specifications of any mobile phone. 

Intent-Based Sentiment Analysis 

Just as the name suggests, intent-based sentiment analysis is one of the types of sentiment analysis that focuses on the true intentions of a customer feedback. It goes beyond traditional sentiment analysis. While sentiment analysis focuses on understanding whether a piece of text expresses positive, negative, or neutral emotions, intent-based sentiment analysis digs deeper and uncovers the intentions behind those emotions. It aims to determine how someone feels and why they feel that way. 

For example, imagine a customer posted a review on an e-commerce website saying he is very unhappy with their product! At first, it looks like a negative sentiment. However, intent-based sentiment analysis can go further and determine if he is unhappy from a faulty product, delivery issues or poor customer service. Once the company has gained insights through these types of sentiment analysis algorithms, they can make the proper decision to enhance their product or services. 

Fine-Grained Sentiment Analysis 

It is one of simplest and most widely used sentiment analysis types to understand the customer’s sentiments. Fine-grained sentiment analysis helps businesses assess the ratings and reviews provided by customers.  

Businesses can use predefined categories such as very positive, positive, neutral, poor, or very poor while analysing sentiments. Furthermore, they use a rating scale to measure feedback from customers.

For example, many companies offer customers to rate their shopping experience, or customer services from scale 1 to 5 or 1 to 10. From 10 being very positive and 1 being very poor.

Emotion Detection

This type of sentiment analysis focuses on identifying and classifying specific emotions expressed in textual data. By analysing the language used and the context surrounding it, these sentiment types can determine if a text conveys happiness, sadness, anger, fear, and many other emotions.

For example, if a person writes, I am so excited about my upcoming vacation! The emotion detection sentiment analysis tool would accurately identify the excitement and positive sentiment of that person. 

Why Is Sentiment Analysis Important? 

Sentiment analysis or opinion mining is an important tool for business intelligence that helps companies enhance their products or services. Here are some of the reasons why sentiment analysis is important: 

Objective Insights 

Sentiment analysis provides businesses with objective insights about how their customers feel and types of sentiment they have about their products or services. It eliminates personal bias with the help of AI and ensures consistent and objective results. This is particularly important for making data-driven decisions. 

Product and Service Improvement

Sentiment analysis helps companies enhance their products and services based on real customer feedback. When they identify the specific aspects or entities that customers feel (positive or negative sentiments) businesses can prioritize areas for improvement. This feedback is invaluable for product development and quality enhancement.pr 

Real-Time Insights 

Sentiment analysis enables businesses to monitor and respond to customer sentiments as they evolve. Marketers can configure the software to provide alerts when negative sentiments are detected for specific keywords or topics, enabling swift responses to crises or market trends. 

Competitive Advantage

Understanding customer sentiment provides a huge competitive advantage. It allows companies to adapt to changing customer preferences and market dynamics quickly. Businesses that can address customer concerns and deliver products or services aligned with customer sentiment are more likely to succeed in the market. 

Customer Engagement

One of the major advantages of using sentiment analysis is that it helps understand customer sentiments and allows for more effective engagement. Companies can reach out to customers, address their concerns, and turn negative experiences into positive ones, which improves customer retention. 

How to Perform Sentiment Analysis? 

Till now, we have learnt about sentiment analysis, types of sentiment analysis, and importance. But the question is how you can perform it? There are some common steps that businesses use to conduct the analysis.

• Data Collection: The first step is to collect the data. You can gather a dataset that includes text data, like customer reviews, social media posts, and customer feedback.
• Text Scrubbing: Now, clean and pre-process the text data by removing noise, such as special characters, numbers, and punctuation marks. Customer reviews and feedback often include unnecessary information that can affect your analysis process. These can include:
  • Stopwords like and, a, or, how, but, etc. 
  • Punctuations like commas, unnecessary periods, exclamatory etc. 
• Data Analysing: At this time, you can use the gathered data and run it through sentiment analysis algorithms to analyse it. The most common classification of analysis will be negative, positive or neutral, as we stated earlier. However, you can dig deeper to identify sentiments like sadness, anger, joy, etc. 
• Word Embedding: After you have analyzed the data, you have to make it understandable for machine learning models. You must convert the text data or phrases into vectors. This step is basically done by professionals like data scientists or data analysts.
• Testing and Training: Once your data is fully prepared, the data science team will train and test your machine learning model. They’ll divide your data into two parts for this- training and testing. When your model can make good predictions about the analysis, you can use it to figure out the sentiments. 

What are the Sentiment Analysis Challenges? 

Despite the huge advancements in AI and ML, they are still way behind while understanding human needs. There are many challenges that machine learning faces, some of them are following: 

Sarcasm Detection 

Sometimes detecting sarcasm is extremely difficult for ML while analyzing the sentiments of a customer. For example, read this review- Yeah, Great! Now I have to travel 2 miles to get my parcel.  

What does the review imply? The customer used sarcasm here, and he is not happy with the delivery. Unless the ML fully understands the scenario, it might be flagged as a positive comment. 

Negation Detection 

If a sentence includes not, neither, non-, un-, dis- etc. it simply means that the overall sentiment is positive. For example, this product is not at any disadvantage! The algorithm may classify this statement as negative, but it is actually positive feedback for the product. 

So, the current sentiment analysis algorithm types may not be fully capable of classifying the negation in sentiment analysis. 

Uses of Emoji 

According to Market Drive, more that 70% of people now use emojis while writing any text. As we know, sentiment analysis works with written text, it cannot fully understand the context of a sentence.

For example, I have tears in my eyes 😂. This sentence suggests that the user is implying tears while laughing. However, the ML will only read the sentence and ignore the emoji, which can totally misinterpret the sentence. 

Multi-Polarity 

This situation occurs if there is more than one sentiment in a sentence. For example, the phone is no doubt amazing, but the color is not impressive.

It becomes hard for the tool to correctly interpret the sentiment. You’ll have to use aspect-based sentiment analysis to fully extract its emotion. 

Key Takeaways 

Sentiment analysis is an amazing way to determine what the feedback of a customer is. So, as we are about to conclude our topic, below are some of the key points to consider: 

• Sentiment analysis uses ML and NLP to assess the emotional tone of digital text. 
• There are four main sentiment analysis types: Aspect-based, Intent-based, Fine-grained, and Emotion Detection.  
• Sentiment analysis helps in product and service improvement by pinpointing the areas for improvement based on customer feedback.  
• Sentiment analysis facilitates more personalized customer engagement, as companies can reach out to address concerns and convert negative experiences into positive ones, thereby improving customer retention.  
• To perform sentiment analysis, businesses follow key steps, including data collection, text scrubbing, data analysis using sentiment analysis algorithms, word embedding, and testing and training machine learning models. 
• Challenges in sentiment analysis include difficulties in sarcasm detection, negation detection, interpreting the use of emojis, and handling multi-polarity in sentences, which can impact the accuracy of sentiment classification. 

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