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4 Use Cases of Sentiment Analysis Machine Learning in 2024

It is not surprising that the use of AI in the workplace has increased by 270% from 2015 to 2019, considering the data available and its exponential growth. Companies implement AI technologies to enhance the performance of their services or products in various sectors, and more than 30% of marketers state that they see the most benefits of AI in understanding customers’ needs. 

This article highlights the implementation of AI-powered machine learning algorithms in sentiment analysis and how they help companies make sense of data.

How do machine learning algorithms help sentiment analysis?

Sentiment analysis is a Natural Language Processing (NLP) method that categorizes content based on the emotional tone as either positive, negative, or neutral. However, manually analyzing the sentiment in texts, phone calls, or reviews is almost impossible, especially when the data accumulates.

Machine learning algorithms can model many features and adapt to adjusting input. That’s why companies implement machine learning or deep learning algorithms to fasten business processes and get insights to develop new strategies. 

4 machine learning approaches that can be applied to  sentiment analysis

1. Supervised learning

In supervised learning, the data is labeled manually by the annotators, and it is used to train the algorithm. Thus, the algorithm can classify incoming, unlabeled data based on pre-labeled data. This method outperforms both semi-supervised and unsupervised methods as it depends on data labeled manually by humans and includes fewer errors. 

Some supervised algorithms are as follows:

  • Support Vector Machines (SVM)
  • Naive Bayes (NB)
  • Logistic Regression (LogR)
  • Maximum Entropy (ME)
  • K-Nearest Neighbor (kNN)
  • Random Forest (RF)
  • Decision Trees (DT)

2. Semi-supervised learning

Semi-supervised learning uses both labeled and unlabeled data, and because it doesn’t require as much human intervention as supervised learning, it takes less time to conduct analysis. Unlabeled data assists in extracting language-invariant features, while labeled data is utilized as a classifier.

3. Unsupervised learning

Unsupervised learning is a lexical-based approach where the data is clustered based on shared characteristics, including word pairings or popular terms. It does not need training data or modeling and instead uses predefined lists or dictionaries.

4. Deep learning algorithms

Deep learning algorithms depend on neural networks and outperform other machine learning methods. However, they require a great amount of data to train the model. Thus, they give the best results when applied to large datasets.

Some common deep-learning methods are:

  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Deep Belief Networks (DBN)
  • Long-Short Term Memory (LSTM)


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Source: Artificial Intelligence Review

Figure 1. Comparison of common approaches in sentiment analysis

Feel free to read our latest overview of sentiment analysis methods.

4 Use Cases of Machine Learning in Sentiment Analysis with Case Studies 

1. Sentiment toward smartphone brands

In a study, the five most popular smartphone brands, namely Samsung, Apple, Huawei, Oppo, and Xiaomi, and the sentiment in customer reviews towards these brands have been investigated based on 9000 customer reviews.

Researchers combined various machine learning models such as Support Vector Machines (SVM), Multilayer Perceptron Neural Networks (MLP Neural Nets), Naive Bayes (NB), and Decision Tree (DT) algorithms to measure the accuracy of predicting sentiment scores. 

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Source: Applied Artificial Intelligence

Figure 2. The comparison of five popular smartphone brands based on their price range as either high, mid, or low

As illustrated in Figure 2, for high-priced smartphones, customers have more positive sentiments toward Apple and Samsung products. For the low price range, Samsung smartphones have the highest customer satisfaction.

2. Improving product recommendations using customer experience/sentiment

sentiment analysis machine learning

Source: Complex & Intelligent Systems

Figure 3. The visualization of the proposed classification system

For this research, 142+ million reviews are gathered from Amazon, Shop clues, and Flicker websites. Researchers use a machine learning-based regression model to analyze the sentiment in reviews to design a new hybrid recommendation system (HRS). 

Three metrics evaluate the proposed HRS performance: mean absolute error, mean squared error, and mean absolute percentage error. The results show that the mean absolute percentage error for the HRS is 98%, which indicates great accuracy.

To sum up, HRS accurately predicts customer sentiment when shopping from a particular supplier. 

For those interested, here is our article on applications of sentiment analysis in the e-commerce industry

3. Sentiment toward bitcoin and price changes

Customer reviews on both Twitter and Reddit provide great insights regarding customer sentiment. In this study, the data was extracted from Twitter and Reddit. Researchers analyzed the correlation between Bitcoin price changes and public sentiment on social media by implementing Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) techniques.


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Source: ArXiv

Figure 4. The visualization of how Bitcoin data and data from social channels have been merged

Researchers found that their model provides more accurate results than the traditional methods as their Root-Mean-Square Error is lesser.

Check out our article to learn more about cryptocurrency sentiment analysis and how it works.

4. Comparison of ML methods on the classification of Youtube reviews

In this work, researchers compare some widely used machine learning methods, Decision Tree (DT), K-Nearest Neighbors (kNN), and Support Vector Machine (SVM), to see how accurately they predict the public sentiment in Youtube reviews. Results show that the accuracy scores are 73%, 80%, and 93%, respectively, and SVM provides the most accurate results.


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Source: IJEAT

Figure 5. The comparison of the accuracy of different machine learning methods on Youtube reviews

Learn the benefits of social media sentiment analysis through our article.

You can also check our data-driven list of sentiment analysis services to determine which option fulfills your company’s needs.

Feel free to contact us if you have questions regarding sentiment analysis:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Begüm Yılmaz
Begüm is an Industry Analyst at AIMultiple. She holds a bachelor's degree from Bogazici University and specializes in sentiment analysis, survey research, content writing services and Customer Relationship Management (CRM) systems.

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