Understanding sentiment analysis using machine learning

In today's emotion-driven market, sentiment analysis is invaluable. Sentiment analysis is widely used to gauge customer opinions of a brand or product online. It looks at publicly available reviews or social mentions containing predefined keywords and analyzes the emotions behind the message.

Find out what sentiment analysis using machine learning can do for your business.

Sentiment analysis is a machine learning classification technique that analyzes text to determine whether it is positive, neutral, or negative. It is used to analyze natural language to uncover emotions expressed by your customers or potential clients.

The data sources analyzed are wide-ranging, including blog posts, emails, customer support tickets, survey responses, web chats, forums, and tweets.

Sentiment analysis enables businesses to understand customer experience and ascertain their opinions. It also helps them to:

• Be more responsive to customer feedback

• Get insights that will improve service delivery

• Monitor brand awareness and reputation in real time

• Analyze how new products or services are received by customers

Manually analyzing sentiments in texts is tedious and time-consuming. As a result, businesses use machine learning for this task. Many parts of the text need to be analyzed to make the sentiment analysis as accurate as possible.

Machine learning algorithms can adapt to any input, whether large or small. The software is programmed to understand text in ways similar to human beings. The process involves tagging nouns, verbs, adverbs, or adjectives as positive, neutral, or negative.

Machine learning models are “trained” to identify phrases by firstly processing large volumes of text containing pre-tagged sentiments. The program learns which words or phrases are neutral, positive, or negative.

The automated systems can classify data themselves after being fed a reasonable amount of relevant data. The model will continue to score more phrases, improving its accuracy and ability to perform sentiment analysis.

Supervised and unsupervised machine learning techniques are the most common methods. Other less common techniques are reinforcement and semi-supervised learning. Let's examine each of these.

Supervised learning

Supervised learning is easy to implement and deals with straightforward tasks. Data inputs are labeled with the answer the algorithms should arrive at. This allows the algorithm to classify unlabeled data based on manually pre-labeled data.

Unsupervised learning

Unsupervised learning is where the models learn organically instead of receiving data sets with explicit instructions. The model automatically finds structure in the raw data through analysis and interpretation.

Unlike supervised learning, this technique does not involve the model having access to completely labeled data sets to train the algorithm. Data is clustered based on shared characteristics.

Reinforcement learning

In reinforcement learning, rewards and feedback are used to find the optimal technique for accomplishing a task.

Semi-supervised

Semi-supervised learning combines attributes of both supervised and unsupervised learning, where the process and reference data are known, but the data is incomplete. However, it requires less human intervention than supervised learning.

Machine learning uses classification algorithms for sentiment analysis. They include:

Naive Bayes

This is a supervised learning algorithm in machine learning that helps with text classification. It involves a high-dimensional training data set.

Naive Bayes is based on Bayes's theory which predicts the text category by calculating words against each other. It uses the assumption that the occurrence of one feature is independent of other features. It then learns the probability of each word, including its features and the groups they belong to.

Probabilities are assigned to words and assigned labels of positive, neutral, or negative.

Linear regression

This is a well-known statistical algorithm used to predict some value (y) based on the value of features (x). It is also part of the supervised learning algorithms family.

The data sets can be examined through linear regression to show a relationship. Linear regression calculates how inputs relate to output which will determine whether phrases are negative, positive, or neutral.

Support vector machines (SVM)

This algorithm is highly effective for categorizing text documents, whether linear or non-linear. It uses supervised learning, which relies on labeled input and output training data, to analyze data and learn patterns.

SVM aims to find a large margin between the different classes. For example, it aims to find the best classification margin for the data at hand. It learns to draw the hyperplane by using the margin-to-maximization principle.

Deep learning

This is a subset of machine learning which learns patterns in unstructured data. Deep learning algorithms attempt to mimic how the human brain works with the help of artificial neural networks.

Deep learning allows the machine to progressively link together several hierarchical human-created processes, as the human brain does. The neural network algorithms learn patterns, understand the context between words, and use them for future reference.

1. Collect data

One of the most difficult aspects of building a model is sourcing labeled data.

The first step is to collect the text to be analyzed. Data must be collected and annotated to produce good results for machine learning sentiment analysis.

Data can be uploaded through a live API, enabling you to glean publicly available data from repositories like Amazon reviews. Data can also be manually uploaded using a CSV (comma-separated values) file. The data is then cleaned to remove noise.

2. Generate embeddings

Before training the model, words need to be converted into word embeddings, which are just numerical representations of words to enable the model to learn.

Word embeddings are vector representations that capture the context of the underlying word in relation to other words in the sentence. There are two ways to generate word embeddings:

• Using pre-trained word embedding

• Learning from scratch

3. Model architecture

The next step is to choose the model architecture you will use. There are different machine learning model architectures for different purposes. Choosing a model architecture will depend on the business requirement, complexity, and volume of the data set.

A recommended approach is to start with a simple model architecture and gradually advance to more complex ones. Recurrent neural networks or clustering architectures are recommended for sentiment analysis.

4. Model parameters

A model parameter is a variable that can be estimated from given data. They are required when making predictions and are learned from data. Examples include coefficients of linear regression models and cluster centroids in clustering.

5. Train and test the model

During this phase, you will ascertain the validity of the model. This involves checking whether it can identify phrases, and organize, analyze, and correctly interpret sentiments.

Model training involves several iterations of testing and comparison to get the desired results. To train the model, you will need large volumes of pre-processed data to reduce null values. During training, model parameters are continuously updated.

6. Run the model

After running tests and assessing the validity of the model, it is ready for use. The model can be fed new text documents for sentiment analysis.

Here are some real-world examples:

Analysis of social media comments

Social media listening is probably the most common application of sentiment analysis. 

Sentiment analysis tools collect data from customer conversations or social media mentions and use the insights gathered to take action. Reviews and comments are analyzed and categorized as positive, negative, or neutral.

Through analyzing the comments, you can uncover honest reviews about products. The model can also detect sarcasm and understand acronyms.

Understanding market response to a product or service

Knowing how your customers feel about your product or service is crucial. Companies use sentiment analysis to learn what customers say about their products. With these insights, they have a better picture of areas that need improvement.

For instance, if a business launches a product and users are complaining about it, the team will analyze the comments to discover which changes are needed to the product.

Monitoring brand perception

In the age of technology, anyone can express their opinion about a brand online. Therefore, brands have to monitor what customers are saying about them to remain relevant and able to provide what people want.

Opinions about your product or service may be spread across user-generated videos, social media posts, or online reviews. With the right sentiment analysis tool, brands are alerted in real-time about negative sentiments, and they can act quickly before further damage is done to the brand's reputation.

Listening to the voice of your customer

Sentiment analysis allows businesses to discover recurring themes in customer survey results. Responses can be analyzed to uncover their polarities, i.e. whether they are positive, negative, or neutral.

The supervised machine learning technique best suits sentiment analysis because it can train large data sets and provide robust results. It is preferable to semi-supervised and unsupervised methods because it relies on data labeled manually by humans so includes fewer errors.

The three main approaches to sentiment analysis are rule-based, automated, and hybrid systems. Rule-based sentiment analysis tags input data based on a set of predefined rules. Automated systems use machine learning algorithms to classify sentiments. Hybrid systems use rule-based and automated systems to identify the tone of a sentiment.