How Does Text Sentiment Analysis Work?

Text Sentiment Analysis Work – The best way to boost your company’s sales is by connecting with customers. You can develop better strategies when you are aware of people’s sentiments. One unique way of understanding your client base is by decoding the feelings behind their feedback, and you can do it with the help of text semantic analysis.

It is a new and popular technique that interprets an individual’s emotional state. Companies use feedback from clients to understand whether they have negative, positive, or neutral sentiments about a particular product or service. Most corporations have improved their position within the market by using such analysis techniques.

Keep reading the article to get a detailed insight into what is text semantic analysis? How is it used? What benefits can one get from the technique? And how high its accuracy is. Once you know about the technique, you can easily use it for your successful business venture.

What is Text Sentiment Analysis Work?

Text sentiment analysis is a technique that extracts and analyzes valuable information from data such as online reviews. You could even use emails, direct messages, or information from chatbots to understand the consumer’s feelings. Through this process, an individual can detect negative or positive sentiments in the text. By understanding the customer’s emotions, a company can determine the satisfaction level of users. Moreover, they can work on weak areas and enhance the overall customer experience.

Several companies use sentiment analysis tools for their benefit. It gives them a detailed insight into how the customer feels at a particular moment. All that one needs is a scoring mechanism, which will allow them to judge various pieces of text. Nowadays, sentiment analysis is useful for analyzing comments in news articles and blog posts. With this single mechanism, you can capture the client’s opinion and figure out his attitude towards your product or people.

How does Text Sentiment Analysis Work?

You cannot find a better way to understand your audience than text semantic analysis. It uses natural language processing (NLP) along with machine learning algorithms. Using these methodologies, one can find the hidden sentiments within text chunks.

Once these intelligent systems have analyzed a text, they can conclude whether the message is negative, positive, or neutral. There are different sentiment analysis models used for this purpose, including:

  • Rule-based method: It uses human-crafted rules for analyzing sentiments in a text. Most of the time, sentiment lexicons are included within the rules. The system looks for words from the sentiment dictionary and then adds the scores. Afterward, an average value is calculated.
  • Supervised machine learning: For this technique, the system receives a labeled data set having documents whose sentiments have already been identified. The system learns the given classifiers and analyzes incoming raw data.
  • A combination of two models: If the previously mentioned models do not suit your needs, you could opt for a hybrid approach. It uses both techniques.

A system can decode the human language and provide meaningful insight using these mechanisms.

Role of Sentiment Libraries in Text Semantic Analysis

Sentiment libraries are collections of phrases, words, and documents. They are manually scored by expert data scientists. Once a system has a sentiment library to consult, it can use the existing information to label unknown data.

For a system to work effectively, a sentiment library is mandatory. Until one doesn’t provide an accurate library to a system, it will be unable to detect correct sentiments and scores for various phrases. Furthermore, if your system caters to multiple languages, you must have a library for each language.

After a library is personalized, decide upon the rules for the system. These rules are necessary for the analysis process.

Benefits of Sentiment Analysis

Sentiment analysis is a suitable mechanism for finding public opinion. You can use it in multiple ways. The best uses of the system are:

To Counter the Negative Emotions: An advantage of opting for sentiment analysis is that it lets you recognize the problem faced by users. Maintain a dashboard for negative mentions and work on a solution to save your reputation the minute you see a rise in the values.

Understanding Public Opinion in Real Time: The best use of text sentiment analysis is that companies can learn about a customer’s opinion in real-time. Whether organizations launch a new campaign or introduce a service, audience reviews will be analyzed to see if there are any negative sentiments detected.

Enhance Customer Experience: Analyzing customer feedback also informs companies about how clients feel regarding their products. These negative comments allow a brand to work on its weak areas.

To Find Out the Company’s Position: Along with other features, companies can use text sentiment analysis to find their position in the market. Brands could keep an eye on the ratio of negative and positive sentiments within the feedback and take decisions accordingly.

Find the Success Rate of Campaigns: Just as the system helps brands monitor their growth and popularity, it can also assist them in keeping track of the success rate of a campaign. If the audience has more negative sentiments toward a campaign, the brand needs to come up with a new technique.

Analyze Competition: Lastly, companies tend to understand the competition they face within the market through this analysis process. You can’t benefit from negative, positive, or neutral sentiments until you don’t set a benchmark for yourself. So, you can see the various sentiment analysis for different companies and compare them with your results.

How High is the Accuracy of Text Sentiment Analysis?

You might have doubts about the accuracy of the system. Being in the real world, you can’t take chances with anything.

Measuring human sentiments is complicated and might not always be a hundred percent correct. According to research, the accuracy rate for sentiment analysis is about 65% to 80%. Since sentiments are subjective so measuring their accuracy can be a problem. Several factors prove to be a challenge for the analysis process, like:

  • Sarcasm: it is tough to determine whether a person is being sarcastic or serious
  • Multipolarity: A single sentence can say something positive about one item and negative about the other.
  • Negations: Reversing the meaning of a phrase


Along with these common issues, machines also face challenges in figuring out the context and subjectivity within a comment. These limitations make sentiment analysis tools struggle to detect the hidden emotions in a text.

Still, the system provides the results and makes your life much easier.

Using Text Semantic Analysis to Your Advantage:

In today’s time, customers matter more than anything else. If you can’t provide people with the best experience, they won’t return to you. That is why sentiment analysis is essential for you. Instead of just taking feedback or reading comments, you can move a step ahead and get to know how people feel about your brand.

Text sentiment analysis work will break text blocks into chunks and interpret the emotion felt by the audience at a particular time. As you read earlier, the system uses NLP and machine learning algorithms, so lesser chances of error.

Even if it’s not a hundred percent correct, the accuracy is still higher than most techniques. You even get to choose from various models based on your needs.

With the help of these advanced systems, you won’t need to do any hard work. Each customer feedback will be automatically analyzed, and you will learn where you need to improve. No matter how massive your client list might be, you can discover the emotions of individuals at every stage without any delays. VizRefra offers solutions in Sentiment Analysis from different sources of your choice, either plain text, social media posts or latest news headlines.