Sentiment analysis is used to gain understanding of the opinions, emotions and attitudes in a text. Also known as sentiment classification or opinion mining, sentiment analysis allows you to determine whether a piece of content is positive, negative or neutral by extracting particular words or phrases. The main purpose of sentiment analysis is to analyze the public’s opinion of certain products, events, people, or ideas.

Significant advances have been made in the field over the past few years, largely due to an unprecedented growth in user-generated sentiment analysis data. It now has countless applications, offering insights for big business, politics, psychology, and sociology.

This guide is meant to serve as an overview of sentiment analysis: the fundamentals, various types of sentiment classification, how it works, as well as its applications and the challenges faced by the field. Let’s jump right in!

An Introduction to Sentiment Analysis

What is a sentiment?

As opposed to objective fact, sentiments are subjective expressions used to describe a person’s feelings towards a particular subject or topic. While ’emotion’ and ‘sentiment’ are used interchangeably by many, there is a fundamental difference between the two concepts. Sentiment implies a more organized disposition towards a target, while emotion describes an involuntary physiological response.

In text, sentiment can be expressed in two different ways. It can be explicit, where an opinion is directly expressed (e.g. “This chocolate is delicious”), or implicit, where the text implies opinion (e.g. “My iPhone broke in just a week”). Most sentiment analysis research focuses on explicit sentiment, as it is often easier to spot and analyze.

The target of a sentiment can be an object, a concept, an event, a person or just about anything. In the case of movie or product reviews, it’s generally quite easy to spot the topic of the text.

To measure opinions, a sentiment score usually consists of two aspects: polarity and intensity.

Sentiment polarity refers to the orientation of the sentiment (from negative to positive).

refers to the orientation of the sentiment (from negative to positive). Sentiment intensity describes the strength the of sentiment (from low to high).

Why is sentiment analysis important?

Whether you’re conducting market research or making simple everyday decisions, we often look to other people’s opinions before making our own choices. Web-based platforms containing millions of movie ratings, forum threads, social media posts, consumer reports, and restaurant reviews have made access to consumer opinions readily available to anyone on the Internet. This abundance of user-generated content presents a treasure trove of information ripe for text analysis.

A good sentiment analysis engine can automatically transform raw, unstructured content into structured data that provides an overview of how products, services, or brands are received. This data can in turn be extremely useful for commercial applications, policymakers, social scientists and more. By actively monitoring consumer attitudes and opinions, end users are able to detect subtle shifts in opinions and adapt readily to meet the changing needs of their audience.

Goals of Sentiment Analysis

Sentiment analysis can be further simplified into two separate tasks:

Subjectivity classification: Classification of text as objective or subjective. Polarity classification: Classification of text as positive, negative, or neutral opinion.

This process occurs at many structural levels, from entire documents to individual words. Choosing the appropriate level of sentiment analysis for your use cases greatly enhances the accuracy and coverage of your model. In general, sentiment analysis occurs at three different levels:

Document-level: At this level, the goal is to classify an entire document’s sentiment. For example, in the case of customer reviews on ecommerce platforms, the system determines whether each entire review expresses an overall positive or negative opinion of the product.

At this level, the goal is to classify an entire document’s sentiment. For example, in the case of customer reviews on ecommerce platforms, the system determines whether each entire review expresses an overall positive or negative opinion of the product. Sentence-level: A slightly more detailed analysis, the task of sentence-level sentiment analysis generates a score for each sentence. This provides greater accuracy and coverage than document-level analysis.

A slightly more detailed analysis, the task of sentence-level sentiment analysis generates a score for each sentence. This provides greater accuracy and coverage than document-level analysis. Entity/aspect level: Rather than looking at language constructs, such as paragraphs or sentences, aspect-level sentiment analysis takes a more granular approach by looking directly at the opinion itself. At this level, all entities are analyzed and a fine-grained sentiment score is assigned to each.

Approaches to Sentiment Analysis

There are two major approaches to large-scale sentiment analysis:

Rule-based approaches: Also known as semantic approaches, these algorithms perform sentiment analysis based on a set of manually defined rules.This is often a top-down approach, as you’re trying to write rules that emulate the knowledge of a domain expert.

Also known as semantic approaches, these algorithms perform sentiment analysis based on a set of manually defined rules.This is often a top-down approach, as you’re trying to write rules that emulate the knowledge of a domain expert. Automatic approaches: This describes any approach that relies on machine learning techniques to extract, identify or characterize the sentiment of a text. These techniques are often more inductive, finding patterns and regularities and using them to generate structure rather than emulating the mind of an expert.

Each of these approaches requires a considerable human involvement, at least initially. In order for sentiment analysis models to work, human annotators have to first label their perception of the sentiment of individual words or short texts. This sentiment labeling is language, domain, and even topic-specific.

Sentiment Analysis Applications

There are a huge variety of use cases for sentiment analysis. In particular, the technology is having a transformative impact on customer-facing business areas. From building strong relationships with customers to resolving customer service issues, there are a wide range of ways in which sentiment analysis can improve your operation. Some of its most common applications are in the following areas:

Customer Service: One of the most valuable aspects of sentiment analysis is its ability to categorize texts according to their content. Sentiment analysis tools enable you to parse and sort customer queries according to urgency of tone and strength of emotion, allowing your team to tackle the least satisfied customers first. Similarly, using sentiment classification to monitor your feedback channels can show you which aspects of your service are the most problematic for your clients.

One of the most valuable aspects of sentiment analysis is its ability to categorize texts according to their content. Sentiment analysis tools enable you to parse and sort customer queries according to urgency of tone and strength of emotion, allowing your team to tackle the least satisfied customers first. Similarly, using sentiment classification to monitor your feedback channels can show you which aspects of your service are the most problematic for your clients. Social Media: Sentiment analysis is particularly suited to analyzing your social media branding. While many existing analytics are able to measure quantity of conversation, sentiment analysis also allows you to measure quality of conversation. In other words, it shows you exactly what all those people are saying about your organization. Using machine learning, it’s possible to figure out what causes engagement and measure that engagement in real time. This allows you to move quickly to capitalize on positive sentiment and directly respond to issues as they evolve.

Sentiment analysis is particularly suited to analyzing your social media branding. While many existing analytics are able to measure quantity of conversation, sentiment analysis also allows you to measure quality of conversation. In other words, it shows you exactly what all those people are saying about your organization. Using machine learning, it’s possible to figure out what causes engagement and measure that engagement in real time. This allows you to move quickly to capitalize on positive sentiment and directly respond to issues as they evolve. Marketing: The ability to build an emotional profile for certain customer demographics is extremely valuable for your marketing team. Sentiment analysis can provide a range of useful analysis by looking at product or service updates and their online reception. From comparison of sentiment towards your brand in different markets to in-depth study of how your clients react to press releases, there are a range of ways that sentiment analysis can help you to align your brand with customer expectations. Of course, this doesn’t just apply to your data. Sentiment analysis can also help you to learn from the competition. By studying how people engage with your competitors, you can uncover further areas of improvement, discover your key differentiators, and avoid making the same mistakes as them.

This list barely scratches the surface of what’s possible when it comes to sentiment analysis. As the technology continues to develop, further uses for it will become possible. What’s certain is that sentiment analysis is an extremely valuable tool with uses across your organization, both now and in the future.

However, building a sentiment analysis tool is not always a straightforward process. Below we’ve added some tips to help you get your project off the ground and ensure you maximize your ROI.

Managing Your Sentiment Analysis Project

Whether you’re working off pre-labeled sentiment analysis datasets or are looking to build your own data from scratch, here are a few tips for getting your project off the ground.

Establishing Guidelines

Clear and simple instructions are crucial for obtaining high-quality sentiment analysis data. This is true even for the simplest annotation tasks. For sentiment analysis projects in particular, text is annotated by asking respondents to label them as positive, negative, or neutral. While this approach works well for simple expressions of sentiment (e.g. “I love coffee”), complex text samples may leave annotators unsure of how to annotate, resulting in inconsistent labels. This is particularly true when it comes to labeling sarcasm, irony, and other complex emotional expressions.

At the start of your project, it’s essential to provide a document that clarifies exactly what you expect, as well as best practices for the annotation process. Annotators will appreciate any further guidance you’re able to provide. One crucial thing to consider is whether you require tags in simple positive / negative / neutral categories, or something more fine-tuned.

Annotation and Quality Controls

At first glance, annotating text for sentiment seems straightforward enough: the annotator should be able to read a text sample and classify it as positive, negative or neutral.

However, determining sentiment expressed in a content sample is not as easy as it seems, and heavily depends on the subjective judgment of human annotators. Furthermore, annotators often disagree with each other, and even an individual is not always consistent in the way that they label data. There are several reasons for this, such as the inherent difficulty of the content, personal bias, or simply poor annotation quality.

To help eliminate human error as much as possible, it’s important to have a team of multiple annotators to estimate ground truth data. Especially in the case of sentiment analysis, there is often no right or wrong answer, making it difficult to measure accuracy. Metrics like Cohen’s kappa (κ), Fleiss’ kappa (K), or Krippendorff’s alpha measure inter-annotator agreement as an indicator of quality. These metrics can be used in analyze labelled datasets and annotation instructions to improve the annotation process and resolve any annotation difficulties.

Text Preprocessing

Consumer opinions are generally expressed in an unstructured, disorganized format. These sources contain varying vocabulary, slang, and context, making manual analysis almost impossible. Transforming text into something an algorithm can digest is a complicated process. At this stage, text analytics and natural language processing are used to identify and extract relevant data for sentiment analysis.

Below are a variety of preprocessing methods commonly used in sentiment analysis:

Cleaning identifies and removes data that is irrelevant to the study. This includes, but is not limited to, unwanted punctuation, ASCII code, capitalization, and stopwords.

identifies and removes data that is irrelevant to the study. This includes, but is not limited to, unwanted punctuation, ASCII code, capitalization, and stopwords. Normalization standardizes text to create more uniform sequences. Stemming and lemmatization remove inflectional prefixes and suffixes (e.g. -ed, -ize, -de) from words, generating the root form. For example. runs, running, ran are all forms of ‘run’, which is the lemma of all these words.

standardizes text to create more uniform sequences. Stemming and lemmatization remove inflectional prefixes and suffixes (e.g. -ed, -ize, -de) from words, generating the root form. For example. runs, running, ran are all forms of ‘run’, which is the lemma of all these words. Tokenization breaks down larger bodies of text into individual words, also called tokens. These words are used as input for other types of analysis or tasks such as parsing.

Conclusion

Growing need for consumer insights will keep sentiment analysis and opinion mining relevant for the foreseeable future. This fast-growing technology has the potential to disrupt a vast range of industries, as well as improve customer experience.

Lionbridge AI is a trusted provider of sentiment analysis training data. With nearly a decade of experience creating data for natural language, speech, communication and multilingual projects, we can help you develop your machine learning model with confidence. Our crowd of more than 500,000 pre-tested contributors are located across the globe and available 24/7, providing access to a huge volume of data across all major languages and file types. Contact us now for a free assessment of how we can improve your model.