What Are the Most Popular Application Areas for Sentiment Analysis?

With advancements in technology, you can now analyze sentiments in textual data. Whether you need to understand customer feedback or comments, opinion mining can help you achieve the task. Through opinion mining tools, you can decode hidden emotions in text. If there are negative or positive sentiments in the text, the system can identify them. The ability of the system allows us to use it in multiple domains, including customer support, managing social media comments, and analyzing client feedback. In this article, we will provide a detailed insight on what are the most popular application areas for sentiment analysis and how you can use them to excel in your line of work.

What is Opinion Mining?

Opinion mining assists in finding hidden emotions in pieces of text. It is an automated process that allows us to analyze any dataset. A message or feedback can contain positive or negative sentiments. However, computers can only interpret the correct emotion with the help of sentiment analysis. The process allows you to analyze thousands or millions of text pieces. We use natural language processing to identify if someone is writing a positive, negative, or neutral message. Companies use the system to analyze customer reviews and comments. It helps them identify their weaknesses and strengths without exerting extra effort.

Using NLP to Decode Negative and Positive Sentiments

Natural language processing helps us determine the polarity of a sentence. However, our computers cannot comprehend human language and emotions. It needs a mechanism to convert human language into machine-understandable code, and NLP performs the task. Natural language processing allows the system to recognize the tone and emotion in a sentence.

The main techniques used with NLP are syntax and semantic analysis. They help NLP assess the meaning of a sentence based on pre-defined rules.

Which Application Areas for Sentiment Analysis are More Popular?

An individual can use opinion mining in almost every industry. Whether you are in the healthcare sector or the e-commerce business, sentiment analysis can help you understand your client’s needs. Some of the most popular application areas for sentiment analysis are:

Social Media Monitoring

Monitoring social media is the best application of analysis technology. Companies can collect social media comments or reviews from channels like Twitter and gather information about their current status. You can even use mentions to see what individuals think about your firm.

Brand Monitoring

Brand monitoring is one of the most crucial applications of a sentiment analysis system. You launch multiple products, campaigns, and services. All these things need monitoring to see how the brand is doing in the market. Through emotion analysis, you can get an insight into customer support. If people like your brand, you will find more positive sentiments. However, if your brand is doing something wrong, the ratio of negative emotions will be higher. In times of crisis, sentiment analysis can help you prevent it.

Stock Market Analysis

A stock market is a popular place for deploying opinion mining. It’s a risky sector, but you can save your company by understanding customer opinions. For example, if you need to purchase company shares and feel confused, try to see how the audience perceives them. More positive intentions mean safer investments.

Market Research

Sentiment analysis allows you to see negative and positive emotions experienced by your clients. So, you can collect public opinion on various products and compare yourself with your competitors. It helps you stay above the competition.

Movie Reviews

Sites like Rotten Tomatoes use sentiment analyzers to perform analysis of movie reviews. They create labels and train data to find the public opinion about a specific movie. Similarly, you can also analyze massive stores of subjective data and find its best and worst features.

Employee Satisfaction

You can use opinion mining to make your employees happier. Sentiment analysis allows you to understand the problems of employees. Thus, you can solve issues in advance. You can collect data through feedback forms and analyze them together. The results will help you discover common problems in the organization.

Product Improvement

If your company wishes to evaluate how well your products are selling in the market, you should seek the help of opinion mining tools. It helps you find the features people like and dislike. Moreover, a thorough analysis report assists you in identifying problems with your product. Afterward, you can timely add changes to your current list of products. It helps improve the popularity of your items.

How Accurate is the Analysis?

Analysis mechanisms are used by several companies, including IBM and Intel. The reason behind this is its accuracy and ability to provide quick results. Most analysis mechanisms give a high accuracy rate for massive datasets. The standard set by experts is 80 to 85% which is the human agreement level. Machines providing results near or higher than the standard value are accurate. With the help of these results, companies can shape marketing plans, improve internal and external management and even find brand strength.

Role of Machine Learning in Sentiment Analysis Tools

Machine learning helps automate and improve the process. Functions like speech tagging can be made effective by introducing machine learning. You can use supervised or unsupervised techniques to enhance the functionality of your analyzer. Furthermore, it helps you solve tricky language problems. The evolution of language has created problems for computers because they can’t interpret complex words or sarcasm in text. For example, sick can have multiple meanings in different situations. Thus, a machine learning model allows the machine to understand the difference.

Opinion Mining in Python

You can build sentiment analysis models in several languages, but python is the easiest option. There are libraries, including NLTK, making text processing easier. In comparison to python, other languages can be challenging. To analyze data in python, you will start by cleaning your required dataset. Lemmatization, tokenization, and classification are some processes performed during preprocessing. After data cleaning, the system scores each sentence based on how negative and positive they are.

Using Sentiment Analysis for Your Benefit

Sentiment analysis is an efficient tool. It helps you find customer emotions from comments and feedback. Moreover, you can improve your company’s position with the help of this tool. The best part, managing data with a sentiment analysis tool is simple. You only have to feed relevant data to your opinion mining model and let the system generate detailed reports. An accurate system provides valuable insights about clients in real-time.

If you need help forming reports or managing your company affairs, contact VizRefra. Our experts can provide sentiment analyst services along with office support. You only need to communicate your problem, and we will devise an adequate solution.