Sentiment analysis is a context-mining technique used to understand emotions and opinions expressed in text, often classifying them as positive, neutral or negative. Advanced use cases try applying sentiment analysis to gain insight into intentions, feelings and even urgency reflected within the content. Using natural language processing (NLP) and harnessing the power of computer algorithms, analysts in fields like marketing, politics and finance can quickly process huge volumes of text to extract high-value insights by understanding sentiment. 

What Are 3 Types of Sentiment Analysis?

  1. Binary Sentiment Analysis
  2. Multi-Class Sentiment Analysis
  3. Granular Sentiment Analysis

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Understanding Sentiment Analysis

Let’s use a simple example to understand sentiment analysis. Imagine our company just launched a new product and we want to gauge whether customers like it. We look online and see people have left thousands of product reviews:

sentiment analysis table with sample reviews in the positive, negative and neutral areas
Image: Eric Kleppen

It would take several hours to read through all of the reviews and classify them appropriately. However, using data science and NLP, we can transform those reviews into something a computer understands. Once the reviews are in a computer-readable format, we can use a sentiment analysis model to determine whether the reviews reflect positive or negative emotions.

sentiment analysis table labelling the table above with positive, negative and neutral markers
Image: Eric Kleppen

By analyzing sentiment, we can gauge how customers feel about our new product and make data-driven decisions based on our findings. This technique provides insight into whether or not consumers are satisfied and can help us determine how they feel about our brand overall.

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How Does Sentiment Analysis Work?

There are a three popular approaches to performing sentiment analysis:

  • Rule-based methods 
  • Machine learning methods
  • Hybrid methods

Depending on the complexity of the data and the desired accuracy, each approach has pros and cons. In general, machine learning-based or hybrid methods have become the most common approach for sentiment analysis because they’re better at handling the complexity of human language compared to rule-based methods.

 

1. Sentiment Analysis: Rule-Based Methods

A rule-based approach involves using a set of rules to determine the sentiment of a text. For example, a rule might state that any text containing the word “love” is positive, while any text containing the word “hate” is negative. If the text includes both “love” and “hate,” it’s considered neutral or unknown. 

 

Benefits of Rule-Based Sentiment Analysis Methods

Rule-based methods are typically fast and simple to implement. They can be very accurate when the text is short and straightforward. We can also use rule-based approaches to generate polarity ranges by creating a list of words and assigning them a positive or negative score. For example, we would create a lexicon like this:

sentiment analysis table with scores associate with different feeling words: good = 1; awesome = 1.5; bad=-1; terrible=-1.5
Image: Eric Kleppen

Then, to determine the polarity of the text, the computer calculates the total score, which gives better insight into how positive or negative something is compared to just labeling it. For example, if we get a sentence with a score of 10, we know it is more positive than something with a score of five. 

 

Limitations of Rule-Based Sentiment Analysis

Rule-based methods can be good, but they are limited by the rules that we set. Since language is evolving and new words are constantly added or repurposed, rule-based approaches can require a lot of maintenance. 

Additionally, these methods are naive, which means they look at each word individually and don’t account for the complexity that arises from a sequence of words. This is one of the reasons machine learning approaches have taken over. Large language models like Google’s BERT have been trained in a way that allow the computer to better understand sequences of words and their context.

 

Sentiment Analysis: Machine Learning Methods

A machine learning approach for sentiment analysis involves training a model using machine learning algorithms based on training data. Sentiment analysis can be formulated into a classification problem where we have two classes:

  • Positive
  • Negative

The algorithm is trained on a large corpus of annotated text data, where the sentiment class of each text has been manually labeled. This type of machine learning is called supervised learning

We can build a training data set that looks like this:

sentiment analysis image of a table labeling certain sentiments as positive or negative. The apple pie is bad = negative but I didn't think the food was bad = positive
Image: Eric Kleppen

Using NLP techniques, we can transform the text into a numerical vector so a computer can make sense of it and train the model. Once the model has been trained using the labeled data, we can use the model to automatically classify the sentiment of new or unseen text data. 

Sentiment analysis illustration of a high-level machine learning workflow for sentiment analysis
The high-level machine learning workflow for sentiment analysis. | Image: Eric Kleppen

 

Benefits of Machine Learning-Based Sentiment Analysis

Machine learning-based approaches can be more accurate than rules-based methods because we can train the models on massive amounts of text. Using a large training set, the machine learning algorithm is exposed to a lot of variation and can learn to accurately classify sentiment based on subtle cues in the text.

We can also train machine learning models on domain-specific language, thereby making the model more robust for the specific use case. For example, if we’re conducting sentiment analysis on financial news, we would use financial articles for the training data in order to expose our model to finance industry jargon.

 

Limitations of Machine Learning-Based Sentiment Analysis

One of the biggest hurdles for machine learning-based sentiment analysis is that it requires an extensive annotated training set to build a robust model. On top of that, if the training set contains biased or inaccurate data, the resulting model will also be biased or inaccurate. Depending on the domain, it could take a team of experts several days, or even weeks, to annotate a training set and review it for biases and inaccuracies. 

Beyond training the model, machine learning is often productionized by data scientists and software engineers. It takes a great deal of experience to select the appropriate algorithm, validate the accuracy of the output and build a pipeline to deliver results at scale. Because of the skill set involved, building machine learning-based sentiment analysis models can be a costly endeavor at the enterprise level.

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Sentiment Analysis: Hybrid Methods

Since rules-based and machine learning-based methods each have pros and cons, some systems combine both approaches to reduce the downsides of using just one. The hybrid approach is useful when certain words hold more weight and is also a great way to tackle domains that have a lot of jargon. 

For example, say we have a machine-learned model that can classify text as positive, negative and neutral. We could combine the model with a rules-based approach that says when the model outputs neutral, but the text contains words like “bad” and “terrible,” those should be re-classified as negative. 

A Quick Guide to Sentiment Analysis. | Video: Edureka

 

Types of Sentiment Analysis

Consider the different types of sentiment analysis before deciding which approach works best for your use case. There are three to choose from.

 

1. Binary Sentiment Analysis

Binary sentiment analysis categorizes text as either positive or negative. Since there are only two categories in which to classify the content, these systems tend to have higher accuracy at the cost of granularity.

 

2. Multi-Class Sentiment Analysis

Multi-class sentiment analysis categorizes text into more than two sentiment categories, such as very positive, positive, very negative, negative and neutral. Since multi-class models have many categories, they can be more difficult to train and less accurate. These systems often require more training data than a binary system because it needs many examples of each class, ideally distributed evenly, to reduce the likelihood of a biased model. 

 

3. Granular Sentiment Analysis 

Granular sentiment analysis categorizes text based on positive or negative scores. The higher the score, the more positive the polarity, while a lower score indicates more negative polarity. Granular sentiment analysis is more common with rules-based approaches that rely on lexicons of words to score the text.

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Applications of Sentiment Analysis

Today, sentiment analysis is applied in many fields including:

  • Marketing
  • Politics
  • Finance

 

Sentiment Analysis for Marketing

Sentiment analysis is popular in marketing because we can use it to analyze customer feedback about a product or brand. By data mining product reviews and social media content, sentiment analysis provides insight into customer satisfaction and brand loyalty. Sentiment analysis can also help evaluate the effectiveness of marketing campaigns and identify areas for improvement. 

In the same way we can use sentiment analysis to gauge public opinion of our brand, we can use it to gauge public opinion of our competitor’s brand and products. If we see a competitor launch a new product that’s poorly received by the public, we can potentially identify the pain points and launch a competing product that lives up to consumer standards. 

 

Sentiment Analysis for Politics

Sentiment analysis is used throughout politics to gain insights into public opinion and inform political strategy and decision making. Using sentiment analysis, policymakers can, ideally, identify emerging trends and issues that negatively impact their constituents, then take action to alleviate and improve the situation.

Understanding public approval is obviously important in politics, which makes sentiment analysis a popular tool for political campaigns. A politician’s team can use sentiment analysis to monitor the reception of political campaigns and debates, thereby allowing candidates to adjust their messaging and strategy. We can also use sentiment analysis to track media bias in order to gauge whether content evokes a positive or negative emotion about a certain candidate. 

 

Sentiment Analysis for Finance

Sentiment can move financial markets, which is why big investment firms like Goldman Sachs have hired NLP experts to develop powerful systems that can quickly analyze breaking news and financial statements. We can use sentiment analysis to study financial reports, federal reserve meetings and earnings calls to determine the sentiment expressed and identify key trends or issues that will impact the market. This information can inform investment decisions and help make predictions about the financial health of a company — or even the economy as a whole.

Similar to market research, analyzing news articles, social media posts and other online content regarding a specific brand can help investors understand whether a company is in good standing with their customer base. For example, if an investor sees the public leaving negative feedback about a brand’s new product line, they might assume the company will not meet expected sales targets and sell that company’s stock.

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Sentiment Analysis Challenges

Although hybrid approaches that combine rules-based and machine learning-based sentiment analysis can be very accurate, there are still many challenges that come with understanding human language:

  • Contextual Ambiguity
  • Irony and Sarcasm
  • Lexical Ambiguity

 

Contextual Ambiguity 

Sentiment analysis can be challenging when the sentiment expressed in a text is contextually ambiguous. For example, say we’re reviewing a vacuum cleaner and leave a review that says, “The vacuum really sucks.” The review could be negative or it could be neutral since it’s a fact that vacuum cleaners suck things up. Machine learning algorithms can struggle to correctly classify the sentiment when they encounter contextual ambiguity.

 

Irony and Sarcasm 

When we use irony and sarcasm in text, it can be difficult for any approach to classify the sentiment correctly because using these rhetorical devices involve expressing the opposite of what you actually mean. For example, saying “Great weather we’re having today,” when it’s storming outside might be sarcastic and should be classified as negative. However, since our model has no concept of sarcasm, let alone today’s weather, it will most likely incorrectly classify it as having positive polarity.

 

Lexical Ambiguity 

Words can have multiple meanings and the sentiment associated with a word can vary based on its context. This is one of the reasons rules-based approaches can be challenging and inaccurate. For example, the word “sick” can have both a positive meaning (e.g., “The music sounds sick!”) and a negative meaning (e.g., “I am sick with flu.”). Especially when dealing with slang, sentiment analysis models can have a hard time making sense of words that have multiple meanings.

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