Sentiment Analysis Machine Learning: Approaches & 5 Examples

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. 

Explore the different approaches for sentiment analysis machine learning, real-life implementation of AI-powered machine learning algorithms as well as traditional machine learning models in sentiment analysis and use case of these sentiment analysis tools:

How do machine learning algorithms help sentiment analysis?

Sentiment analysis helps businesses understand how people feel about their products or services by analyzing written feedback. It goes beyond simple word counts to detect emotional meaning in reviews, comments, and other text. 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. 

Real-life examples of machine learning in sentiment analysis

1. Understanding sentiment toward smartphone brands

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.¹

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. 

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

Source: Applied Artificial Intelligence²

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

Figure 3. The visualization of the proposed classification system

Source: Complex & Intelligent Systems³

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).⁴

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 financial assets

Customer reviews on both Twitter and Reddit provide great insights regarding customer sentiment. 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.⁵

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

Source: ArXiv⁶

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 another 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.⁷ Results show that the accuracy scores are 73%, 80%, and 93%, respectively, and SVM provides the most accurate results.

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

Source: IJEAT⁸

5. Chatbots for guest engagement

Marriott also uses AI-driven chatbots to improve customer service.⁹ Aloft Hotels, a Marriott brand, launched ChatBotlr, a chatbot available via text message. Guests can use it to request hotel services, access information, and connect with staff anytime.

By analyzing guest feedbacks from chatbot, Marriott identifies key themes like room cleanliness, staff friendliness, and amenity quality. This real-time insight helps hotels improve services

6 machine learning approaches that can be applied to  sentiment analysis

1. Deep learning algorithms

Deep learning models, including artificial neural networks, are employed to perform sentiment analysis on large datasets. However, they require a great amount of data to train the model. Thus, they give the best results when applied to large datasets.¹⁰

These models are trained on vast amounts of training data to accurately classify text into positive, negative, or neutral sentiments.

Some common deep-learning methods are:

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

Figure 6. Comparison of common approaches in sentiment analysis

Source: Artificial Intelligence Review¹¹

1.1. 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.¹²

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)

1. 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.¹³

1. 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.

2. Natural language processing (NLP)

NLP techniques are used to analyze human language and extract meaningful insights from textual data. It helps in identifying the underlying sentiment in text data, be it positive, negative, or neutral.

NLP can be used to analyze sentiment in social media data, providing sentiment scores that reflect the overall sentiment expressed in social media posts.

3. Support vector machine (SVM)

SVM is a popular machine learning algorithm used in sentiment analysis models. It classifies text data by finding the optimal hyperplane that separates positive and negative sentiments.

SVM can be used in sentiment analysis software to classify customer feedback into different sentiment categories.

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

4. Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks

RNNs and LSTMs work well with data that comes in a sequence, like audio. They help track how emotions change over time in a recording.

5. Naïve Bayes

Naïve Bayes is a statistical model that estimates the likelihood of a word or phrase being positive or negative. It’s widely used in sentiment analysis due to its straightforward classification capabilities, which enable it to rapidly identify the overall sentiment of a conversation.

6. K-Nearest Neighbor (KNN)

K-Nearest Neighbor (KNN) is an effective classification technique for text, making it suitable for sentiment analysis. KNN utilizes labeled data, typically categorized as neutral, positive, or negative, to accurately classify new, unlabeled data. The principle behind KNN is that similar data points cluster together. This model can identify the sentiment of words and phrases based on their proximity to existing labeled data.

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.

Types of Sentiment analysis via machine learning models

Sentiment analysis extends beyond merely classifying text into positive, negative, or neutral categories. It involves understanding deeper elements such as emotions, urgency, and intent within the text.

1. Fine-grained sentiment analysis

This technique explores the finer details of sentiment present in text, utilizing lexical methods to capture subtle emotions and intensity. Fine-grained sentiment analysis goes beyond basic polarity by focusing on detailed aspects such as the strength of emotions and specific objectives. It can be applied in political discourse, product feedback, customer feedback, and media analysis.

2. Emotion detection

Emotion detection aims to identify distinct emotional states in the text, such as surprise, fear, anger, or joy. This method provides a deeper understanding of the writer’s emotional state and speech emotion recognition, customer insights, virtual reality experiences.

3. Aspect-based sentiment analysis

Aspect-based sentiment analysis focuses on evaluating specific elements or features within a text. It provides a detailed view by categorizing opinions about particular aspects as positive, negative, or neutral. This approach is useful in E-Commerce, financial sector, healthcare, human resources.

4. Intent-based analysis

Intent-based analysis goes beyond detecting sentiment to understand the underlying purpose of the text. This method determines if the text is making a query, raising a complaint, giving an instruction, or expressing a desire, and measures the sentiment associated with these intents. Its uses include healthcare, legal documents, employee feedback, marketing evaluation.

5. Multilingual sentiment analysis

Sentiment analysis across different languages involves addressing challenges related to diverse linguistic contexts. This approach requires significant resources and preprocessing, including creating sentiment lexicons and handling translated text. Key aspects include custom model development, automatic language detection.

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.

FAQs

External Links

• 1. Imtiaz, M. N., & Ben Islam, M. K. (2020). Identifying significance of product features on customer satisfaction recognizing public sentiment polarity: Analysis of smart phone industry using machine-learning approaches. Applied Artificial Intelligence, 34(11), 832-848.
• 2. Imtiaz, M. N., & Ben Islam, M. K. (2020). Identifying significance of product features on customer satisfaction recognizing public sentiment polarity: Analysis of smart phone industry using machine-learning approaches. Applied Artificial Intelligence, 34(11), 832-848.
• 3. Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems, 6(3), 621-634.
• 4. Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems, 6(3), 621-634.
• 5. Raju, S. M., & Tarif, A. M. (2020). Real-time prediction of BITCOIN price using machine learning techniques and public sentiment analysis. arXiv preprint arXiv:2006.14473.
• 6. Raju, S. M., & Tarif, A. M. (2020). Real-time prediction of BITCOIN price using machine learning techniques and public sentiment analysis. arXiv preprint arXiv:2006.14473.
• 7. Bansal, A., Gupta, C. L., & Muralidhar, A. (2019). A sentimental analysis for youtube data using supervised learning approach. International Journal of Engineering and Advanced Technology (IJEAT), 8(5), 1-12.
• 8. Bansal, A., Gupta, C. L., & Muralidhar, A. (2019). A sentimental analysis for youtube data using supervised learning approach. International Journal of Engineering and Advanced Technology (IJEAT), 8(5), 1-12.
• 9. Marriott International’s AI-powered Chatbots on Facebook Messenger and Slack, and Aloft’s ChatBotlr, Simplify Travel for Guests Throughout Their Journey.
• 10. Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial intelligence review, 1-57.
• 11. Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial intelligence review, 1-57.
• 12. Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial intelligence review, 1-57.
• 13. Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial intelligence review, 1-57.