Sentiment analysis is an artificial intelligence (AI) technique that helps software determine how people feel in social media posts and blogs. By analyzing the words used, AI can tell and record what their sentiments are as well as why they felt that way based on the subject matter or context.
Many companies eager to find out how people feel about their products now use sentiment analysis. The technique helps them plan what they need to do to improve how consumers perceive their brand.
Read More about Sentiment Analysis
Sentiment analysis relies on several natural language processing (NLP) algorithms, which we listed below.
What NLP Algorithms Are Used in Sentiment Analysis?
1. Rule-Based Systems
Sentiment analysis relies on rules. Since a system does not consider the sequence of words, it requires additional processing of expressions to determine a person’s sentiment correctly. Rule-based sentiment analysis follows this basic process.
- Define two polarized words or opposites (e.g., a negative term such as “bad” and a positive one such as “good”).
- Count the positive and negative words that appear in a given text.
- If the positive word appears more than the negative one, the system returns a positive sentiment and vice versa. If the numbers are equal, the system will return a neutral feeling.
2. Automatic Systems
These systems rely on machine learning (ML) techniques. They determine sentiments based on the inputs they’re fed. In this approach, the system gets fed data to classify a view correctly.
3. Hybrid Systems
Hybrid systems use a combination of rules and ML approaches to ensure a high level of accuracy.
How Does Sentiment Analysis Work?
As discussed, sentiment analysis uses NLP algorithms to develop software that can understand the different ways context can influence sentiment.
First, developers create a text classifier algorithm that can detect if a post contains specific sentiment indicators. They then train these classifiers by introducing a range of positive, negative, and neutral words.
The process usually begins by crawling text and splitting it into basic components, such as sentences, phrases, tokens, and entities. Once that’s done, topic and relationship (i.e., of words with one another) identification follows. The program then assigns a sentiment score to a post. That score can range between -1 (negative) and +4 (positive). Neutral sentiments are often given a score of 0.
Since the human language is complex, it is also often necessary to train programs to detect and analyze grammatical nuances, slang, cultural variations, and misspellings, making the process extra challenging.
Here’s a simple video that shows what sentiment analysis is and how it works.
What Is Sentiment Analysis Used For?
Getting meaningful insights from people is critical to any organization’s success. As such, it has become common to use sentiment analysis for:
1. Social Media Monitoring
With the growing use and reach of social media, platforms have become a means for consumers to air their experiences. Sentiment analysis can help organizations gauge how they’re faring compared to competitors among social media users. Somehow, listening to social media chatter can help marketing departments address challenges that crop up in negative sentiments or emulate best practices indicated in positive views.
2. Brand Monitoring
Brands are considered assets by any organization. That is precisely why almost all companies monitor brand mentions and later on classify them as either positive or negative. Sentiment analysis can also help a business assess brand reputation changes over time. These uses allow companies to address crises better and maintain their good standing.
3. Customer Feedback Monitoring
Organizations can also rely on sentiment analysis to better understand customer needs. They can then lessen negative sentiments by considering feedback. They can also use the data to see how departments are interacting with clients. And if issues crop up, these can be prioritized as areas for improvement.
Sentiment analysis can immensely help organizations improve their operations by addressing relevant issues that may arise from bringing their products and services online. It helps them monitor social media, brand, and customer feedback better.
What Are the Different Types of Sentiment Analysis?
Sentiment analysis, also known as “opinion mining,” can come in many types based on their complexity and the scope of the sentiments evaluated. Here are some of its common types.
- Binary sentiment analysis: This is the simplest form of sentiment analysis that aims to determine whether the sentiment expressed is positive, negative, or neutral. It involves classifying the text into one of these three categories.
- Multiclass sentiment analysis: In this approach, the sentiment analysis model categorizes the text into multiple sentiment classes, such as very negative, negative, neutral, positive, and very positive. This approach provides a finer-grained understanding of sentiment.
- Emotion detection: Emotion detection goes beyond simple positive/negative sentiment classification to identify specific emotions expressed in the text. Emotions can include happiness, sadness, anger, fear, and more.
- Aspect-based sentiment analysis: This approach focuses on identifying sentiments related to specific aspects or features of a product, service, or topic. It provides insights into how users perceive different aspects.
- Fine-grained sentiment analysis: This type of analysis delves even deeper, assigning sentiment scores or labels to specific phrases, sentences, or even individual words within the text, allowing for a more nuanced understanding of sentiment distribution.
- Sarcasm detection: Sarcasm is a form of sentiment that can be challenging to detect due to its often ironic or contradictory nature. Sarcasm detection involves identifying instances where the sentiment expressed opposes the text’s literal meaning.
- Intent detection: While not exclusively a form of sentiment analysis, intent detection involves determining the intended sentiment or purpose of a piece of text. It is often used in customer service to understand if a user’s message is a complaint, a query, a suggestion, or something else.
- Contextual sentiment analysis: This approach takes into account the text’s context and external factors that may influence sentiment. For instance, a statement can be positive in one context but negative in another.
- Temporal sentiment analysis: Temporal sentiment analysis involves analyzing how sentiments change over time. It can help track sentiment trends, identify shifts, and understand the impact of events on sentiment.
- Domain-specific sentiment analysis: Sentiment analysis can be tailored to specific domains, such as finance, healthcare, politics, or technology. Language and sentiment expressions can vary significantly across domains, requiring specialized models.
Why Is Sentiment Analysis Important?
Sentiment analysis holds significant importance across various domains and industries due to its ability to extract valuable insights from textual data. It is important because it can:
- Provide business insights
- Improve customer engagement
- Enhance market research
- Enrich a brand’s reputation
- Aid in product development
- Contribute to competitor analysis
- Be helpful in political and social analyses
- Chip in to financial market analysis
- Fortify customer support
- Fine-tune content creation
- Help understand patient feedback and reviews
- Measure public relations (PR) performance
Sentiment analysis lets organizations harness the power of unstructured textual data to gain insights, make data-driven decisions, and improve customer satisfaction. It plays a crucial role in enhancing communication, engagement, and understanding in a wide range of industries and applications.
- Sentiment analysis helps companies find out how people feel about their products and services. It helps them improve how consumers perceive their brand.
- Sentiment analysis employs rule-based, automatic, and hybrid NLP algorithms to work.
- Social media, brand, and customer feedback monitoring are just three of the many real-world applications of sentiment analysis.
- Sentiment analysis can take many forms, including binary sentiment analysis, multiclass sentiment analysis, emotion detection, aspect-based sentiment analysis, fine-grained sentiment analysis, sarcasm detection, intent detection, contextual sentiment analysis, temporal sentiment analysis, and domain-specific sentiment analysis.
- Sentiment analysis holds significant importance across various domains and industries due to its ability to extract valuable insights from textual data.