Sentiment analysis

Sentiment analysis is a technology within artificial intelligence (AI) that uses capabilities within machine learning and natural language processing to interpret and analyze the emotional tone of text. Identifying whether text has a positive, negative or neutral tone can speed up analysis of large textual datasets and allow for an incredibly efficient overview.

What is sentiment analysis?

Sentiment analysis definition: Sentiment analysis is the ability of machines to analyze and interpret the attitude of a text’s author.

When humans write something, we instill it with meaning, including the sentiments and emotions we feel at the time. While nuances of tone can be quite subtle yet interpretable by other humans, machines traditionally struggle with this task.

Sentiment analysis mitigates this challenge by providing valuable insights into the emotional tone of textual data. It is the computational process of analyzing large quantities of text to determine the underlying sentiment. Typically, this sentiment is categorized as negative, neutral or positive.

How does sentiment analysis work?

Sentiment analysis is a subset of natural language processing (NLP), which enables discernment of tone, attitude and sentiment. NLP technology equips computers with the power to process and understand human language, in text or speech form. With this capability, machines can be trained to identify and extract sentiment from text.

The sentiment analysis process begins with preprocessing. During this stage, the computer system identifies keywords within the input text. This process comprises three core steps:

• Tokenization splits a sentence into disparate elements, such as words and phrases.
• Lemmatization converts conjugated and adapted words into their root forms.
• Stop-word removal filters filler words, leaving only the essential vocabulary.

Once the preprocessing is complete, two distinct approaches to sentiment analysis can be used or combined for a hybrid approach:

• Rule-based sentiment analysis trains computers on a set of predefined keywords and phrases that indicate the author’s intent. It then applies this training to the whole text. For instance, positive language keywords could be “pleasant,” “efficient” or “good service,” while negative language keywords might include “overpriced,” “poorly trained” or “slow.”
  With effective training, computers can recognize words and phrases to identify the sentiment of the text and extrapolate its overall attitude.
  Rule-based sentiment analysis is faster, providing efficient outputs based on predefined rules.
• Machine learning sentiment analysis may take a bit longer since it involves using algorithms to train machines and analyzing vocabulary and sentence structures to sense the underlying sentiment. Unlike rule-based models, machine learning models improve over time as they learn from the process and findings.
  Machine learning sentiment analysis is more adaptable to complex datasets and requirements, making it a more flexible and robust version of the technology.
• The hybrid approach takes the best aspects of both approaches. It takes the speed of rule-based sentiment analysis and adds the more adaptable accurate outputs of machine learning sentiment analysis. Greater accuracy and speed do increase the cost of resources, however, as a hybrid approach to sentiment analysis requires more technical capacity, like faster systems and more computer memory.

What is the history of sentiment analysis?

Although sentiment analysis is a subset of natural language processing, its development has been influenced by both linguistic theories and technological advancements over the decades.

• 1900s, early origins: In the early 20th century, linguists began exploring the emotional and subjective aspects of language, laying the groundwork for sentiment analysis by examining how language conveys sentiment and opinion.
• 1950s, beginnings of artificial intelligence: Theoretical foundations for sentiment analysis were established alongside the early development of AI. Researchers began to consider how machines could interpret human emotions and opinions expressed in text.
• 1960s, early computer languages: Development of early programming languages and systems like ELIZA, which could simulate conversation, marked the initial steps toward automated sentiment analysis by processing textual data.
• 1980s, rule-based approaches: Early sentiment analysis systems relied on manually crafted rules and lexicons to identify and classify sentiment in text. These systems were limited by their reliance on predefined rules and vocabulary.
• 1990s, statistical methods: The introduction of statistical methods and machine learning algorithms allowed for more sophisticated sentiment analysis. Researchers began using large datasets to train models that could automatically detect sentiment patterns.
• 2000s, rise of social media: The explosion of social media platforms provided vast amounts of user-generated content, fueling advancements in sentiment analysis. Techniques such as support vector machines (SVMs) and naive Bayes classifiers became popular for analyzing sentiment in social media posts.
• 2010s, deep learning: The advent of deep learning, particularly with the development of recurrent neural networks (RNNs) and convolutional neural networks (CNNs), revolutionized sentiment analysis. Models like BERT and GPT-2 significantly improved the accuracy and nuance of sentiment detection.
• 2020s, multimodal sentiment analysis: Recent advancements have focused on integrating multiple data types, such as text, images and audio, to enhance sentiment analysis. This multimodal approach has led to more comprehensive and accurate sentiment detection across various media.

What are key types of sentiment analysis?

All forms of sentiment analysis follow the same principles of measuring tone in text. However, how this data is applied and how results are presented differ between varying types of sentiment analysis.

• Emotion recognition is an advanced form of sentiment analysis. Instead of simply judging positive, negative or neutral tones, it detects specific emotions in the text. For example , in analyzing customer reviews to identify emotions such as happiness, anger, or sadness, the phrase “I love the new features!” would be tagged as expressing happiness.
• Intent analysis applies textual tone and emotion to business content, seeking to understand where authors are in the decision-making and conversion process. For example, in evaluating customer feedback to determine whether a user is ready to purchase or just gathering information, the phrase “I need more details about the warranty” would indicate an intent to gather information before deciding.
• Graded sentiment analysis, also known as fine-grained sentiment analysis, deepens the sentimental scale beyond negative, positive and neutral, adding more room for nuanced interpretations. For example, using a five-star rating system to capture varying degrees of satisfaction, a review saying, “The product is good but could be better” might be rated as 3 out of 5 stars.
• Aspect-level sentiment analysis is an advanced form of sentiment analysis that evaluates sentiment on a more granular level. By isolating and analyzing specific aspects of text, it provides a more comprehensive and meticulous understanding of the sentiment. For example, in analyzing restaurant reviews to separately evaluate sentiments about food quality, service and ambiance, the phrase “The food was excellent, but the service was slow” would capture positive sentiment for food and negative sentiment for service.
• Multilingual sentiment analysis is a more complicated application of sentiment analysis that identifies sentiments expressed across multiple languages. For example, in analyzing social media posts in multiple languages to understand global customer sentiment, positive sentiments about a product might be identified from posts in English, Spanish and Chinese.

How is sentiment analysis used?

It can be applied to a range of industries and business types to maximize efficiency when processing large textual datasets. By providing an overview of textual documentation, it saves a lot of time and boosts a business’s general understanding of its position.

One use case for sentiment analysis technology is gauging a brand’s reputation through reviews and online posts. By running sentimental analysis on large datasets of reviews, social media snippets and web articles, a business can get a snapshot of how customers and clients perceive it.

Sentiment analysis also improves customer support. Natural language processing generates text responses for online customer support across a range of industries, and sentiment analysis enhances this technology to provide more personalized, accurate responses. By drawing on the correct information and matching the tone, it builds trust between the customer and business.

Businesses can also conduct market research by using sentiment analysis to identify industry trends, spot opportunities for growth or monitor competitors’ performance. By analyzing a wide range of up-to-date textual articles and snippets, businesses can stay ahead in their market.