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sentiment analysis

Sentiment analysis can automatically detect written text and summarize the emotional tone of the words into three categories: positive, negative, or neutral.

In this digital world, it’s a big job to understand customer opinion on any products or services of any category. Whether you try to read every single text data or use a program to analyze them, it takes a lot of time and effort, so here comes sentiment analysis.

For instance, tracks 500000+ news articles daily, imagine if you wanted to understand their audience’s perception of these articles! That’s where sentiment analysis comes in. These tools analyze the emotional tone of text data, allowing to see the overall sentiments of each article.

Analyzing customer reviews through sentiment analysis helps businesses focus on areas for improvement and enhance their products or services by tracking mentions of the brand on social media.

There is a lot to learn about this so users can make informed decisions. Let’s dive deeper to understand it:

What is Sentiment Analysis?

Sentiment analysis is a technique that uses computers’ smart Natural Language Processing to understand the feelings or attitudes people have in written text, figuring out if they’re positive, negative, or neutral. Usually analyzes written text to understand the emotional tone behind the text.

It plays an important role for businesses and market researchers. Both can utilize it to understand public opinion by tracking mentions of their brands. They can check if the conversation leans towards positive or negative feedback on specific topics because it offers valuable insight from the vast amount of text data.

Moreover, sentiment analysis also comes with some limitations, particularly machine sarcasm, irony, and casual language.

How Does Sentiment Analysis Work?

The main technology behind it is machine learning and Natural Language Processing. NLP usually understands the complexities of human language, like slang or sarcasm.

Machine learning is trained on vast amounts of text data that’s already been labeled as positive, negative, or neutral. By training on this data, the algorithms learn to recognize signs in a language that shows how people feel.

There are three methods of sentiment analysis: rule-based, automatic, and hybrid.

1. Rule-based: This one is simpler and faster, where the system depends on predefined lists of words with positive, negative, or neutral.

For example, if a text has the same amount of positive and negative words, then it’s classified as neutral sentiment. But it may not capture the nuances of languages.

2. Automatic: This is a machine learning technique that also works on vast amounts of text data that’s already been categorized by sentiments.

This method is complex but also gives more accurate results, it learns to identify sentiments based on the patterns they discover in the data.

3. Hybrid: This one involves both rule-based and automatic sentiment analysis and gives highly accurate results.

Types of Sentiment Analysis

Your approach depends on what kind of insights you’re looking for in your text data. Here are some common types of sentiment analysis:

1. Subjectivity Classification: This type is the foundation one, which separates factual statements from opinions. It only pulls out whether people are expressing their feelings or beliefs.

2. Polarity Classification: This one goes a step further to subjective statements. It categorizes those opinions as positive, negative, or neutral.

3. Aspect-Based Sentiment Analysis: It focuses on analyzing sentiment towards aspects of products or services. For instance, a restaurant review might mention a great place but a terrible location. Aspect-based will separate these two different opinions.

4. Fine-Grained Sentiment Analysis: This one analyzes even deeper, going beyond just positive, negative, and neutral. It can also detect more emotions, like anger, sadness, or joy.

Benefits of Sentiment Analysis

Understanding Customer Satisfaction:

Sentiment Analysis can measure customer sentiment towards your products or services or text data. It can help businesses analyze customer perception and conversations to find areas of praise and criticism, allowing them to identify what resonates with customers and where they need improvements.

Boosting Brand Reputation:

It also helps businesses stay on top of their online reputation. Social media moves fast, so sentiment analysis tracks mentions of their brands identifies any negative sentiment looming, and addresses concerns promptly. This approach helps the business maintain a brand image.

Making Data-Driven Marketing Decisions:

Marketing efforts are targeted towards what truly connects with the audience. By analyzing how customers react to different messages, this marketing strategy adapts its approach to what resonates most with the audience.

Informing Product Development:

Sentiment analysis of product reviews and social media discussion can provide valuable awareness of what features customers love and what they’d like to see improved, allowing businesses to focus product development efforts on areas with the greatest impact.

Gaining Competitive Advantage:

Through sentiment analysis, businesses or researchers can detect the public’s viewpoint of competitors’ strategies to find areas for improvement and then use that information to develop better solutions and make improvements in the product or service for the market.

Effective Market Research:

Researchers can use it to understand what people are saying about their product or service at online conferences, offering a real-time update on public sentiment on a wide range of topics. This data is necessary for making well-informed decisions.

Tools for sentiment analysis

There are many tools available, that lead to different needs and budgets. Here are some of the popular options to explore:

Full-Grown Platforms:

1. Sprout Social: It analyzes social listening data by offering all categorized text data including social media management solutions with built-in sentiment analysis.

2. InMoment: It is a customer experience platform that uses advanced artificial intelligence to analyze text data from various sources and identify sentiment.

3. Medallia: This uses the advanced feature to detect sentiment by listening features that can pinpoint sentiment not just in the text, but also in speech and video data.

Text-Focused Tools:

4. Google Cloud Natural Language API: This tool can detect sentiment analysis of text data, including for various document types like emails, chats, and social media posts.

5. Amazon Comprehend: It provides sentiment analysis along with other text analysis features for documents, emails, social media, and customer support tickets.

6. MonkeyLearn: It allows you to organize, label, and visualize data like customer feedback for sentiment analysis.

7. Microsoft Azure Text Analytics: This tool offers sentiment analysis along with other text-processing functionalities.

News and Social-Media Monitoring Tools:

8. doesn’t provide sentiment analysis directly but its API provides features that can be helpful for tasks leading up to sentiment analysis on news text data.

9. Brand24: It offers sentiment analysis across various platforms along with web, news, and social media mentions.

10. Talkwalker: This tool offers a “Quick Search’ that analyzes sentiment specifically within the social media data and news.


To conclude, sentiment analysis can be considered one of the most important tools in this digital age, transforming the way we understand the emotions woven into text data. Giving outputs of feelings within written communication offers lots of benefits.

From customer satisfaction and boosting brand reputation to informing product development and gaining a competitive edge, by using businesses and organizations to make smarter decisions.

Various tools are available to suit users’ needs and budgets, from text-analysis platforms to social media monitoring solutions, offering deeper insight into the ever-growing stream of online conversations.

Moreover, it can even tell us if people are happy, sad, or angry from any text data, which helps us know more about what’s going on in the world.


Q1. How accurate is sentiment analysis?

Sentiment analysis is not perfect; sometimes languages can be tricky for machines to understand, especially sarcasm and slang, so it achieves an accuracy level of 70-80%.

Q2. What are some challenges in sentiment analysis?

Sometimes sentiment analysis faces some challenges while giving accurate results because the human language is complex. Here are some specific challenges:

  • Some of the text can be tough for machines to understand, such as sarcasm, irony, and many more.
  • Sometimes the meaning of the word can change the whole scenario of the context, and because of that, it needs to consider the whole picture.
  • Emojis, slang, and figurative language can confuse its tools.

Q3. Is sentiment analysis only used for analyzing customer feedback?

The answer is “no.” It is used for much more than just customer feedback, has a wide range of applications, and is used for social media listening to understand public perceptions about brands and events. It can also be used to analyze movies’ reactions and political discourse, etc.

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