Is Sentiment Analysis Qualitative or Quantitative?

Nowadays, interpreting human emotions is possible. The process is known as opinion mining, and it helps companies understand how their clients perceive their products and services. Opinion mining is the best way to get to know your clients. It assists in running small setups and multinational firms. With the help of the mechanism, you can detect positive, negative, and neutral sentiments in text. Moreover, it also explains the intensity of the emotion. Therefore, using such tools for your brand is a profitable move.

Before going further into the advantages of opinion mining, we should get an idea of how to perform the process and whether sentiment analysis is qualitative or quantitative. Once we get an idea about it in detail, we can use it in a better way.

Defining Sentiment Analysis

Opinion mining involves the interpretation of emotions in text. You provide a relevant dataset to the machine to get a sentiment score. The sentiment score shows how many individuals like or dislike your brand. This way, the process decodes human language.

When someone writes a review or gives a comment on your post, they experience some feelings. These could be negative or positive. Through analysis and interpretation, one can find valuable insight into a client’s feelings. Such systems help companies build a stronger bond with their clients and maintain their brand image. Moreover, an opinion-mining mechanism can even detect neutral emotions in text.

How is Opinion Mining carried out?

Analyzing the text is difficult, especially when it involves Multipolarity and several negations. Luckily sentiment analysis combines natural language processing and machine learning algorithms to interpret the meaning behind any piece of text. It all begins with the data pre-processing. The system breaks down sentences and analyzes every word individually. Each sentence gets a sentiment score, and then a cumulative score is calculated. The overall sentiment score expresses which emotions are in abundance. Furthermore, machine learning and artificial intelligence make the implementation simple. You only add algorithms instead of writing code from scratch. The same task is impossible for a human to carry out. Our human brain cannot process and retain such massive volumes of data. So sentiment analysis models prove to be of great help.

Measuring Emotions in Text

Sentiment scores measure the emotions in a piece of text. Your dataset might have negative, positive, or neutral sentiments. To find the correct intensity of emotions, we score each word. Negative scores indicate negative sentiments, while positive scores are for positive emotions. Higher numbers indicate a higher emotion intensity. In this manner, we quantify emotions to derive accurate results. Even though human emotions are not quantitative, we can get a precise interpretation through association. Machines train on various datasets, which helps them interpret new words.

Should we Categorize Sentiment Analysis as Qualitative or Quantitative?

You can measure quantitative data but not qualitative datasets because emotions are a qualitative value. Most people ask if sentiment analysis is qualitative or quantitative. You won’t find statistics in reviews and feedback. Therefore, through opinion mining, we integrate qualitative data with quantitative values. The meaning of this is, we interpret and score words based on rules. Opinion mining allows us to see emotions in statistical values. Text mining uses lexical signifiers to find the level of negativity and positivity in data. So we take subjective values and represent them in statistical form. For example, firms collect reviews, feedback, and comments from various platforms and analyze them to see if their audience views them in a positive or negative light. The analysis involves scoring every sentence. No matter how descriptive your dataset might be, opinion mining tools can accurately find the hidden emotional meaning.

Types of Analysis

Multiple analysis mechanisms help us find the intensity of emotions and the hidden meaning behind the text. The most common options are:

Standard Sentiment Analysis 

Standard sentiment analysis is the most in-demand analysis type. It classifies text into negative, positive, and neutral categories.

Aspect-based Sentiment Analysis 

Through this option of sentiment analysis, you focus on aspects and features. If you need to analyze product reviews or feedback, opt for aspect-based sentiment analysis. The system categorizes various opinions about the characteristics of products.

Intent Detection 

Intent detection focuses on finding the action behind an opinion. The system interprets the intention of the user behind a comment. This way, you can solve individual concerns efficiently.

Fine-grained Sentiment Analysis 

For finding polarity, nothing is more accurate than fine-grained sentiment analysis. You will retrieve results in positive, neutral, or highly negative values.

Challenges Experienced by Analysis Tools

We face multiple problems in opinion mining. These include the presence of negations, conjunctions, and Multipolarity. A single sentence with negation leads to an incorrect interpretation. Similarly, conjunctions and Multipolarity reduce the accuracy of the system. Another problem in the path of accurate analysis is sarcasm. Humans can understand sarcastic remarks but not computers. Thus, a sarcastic comment often gets a false positive score. We have to cater to these challenges, or our results have errors.

Significance of Opinion Mining

Sentiment analysis can help you in almost every field. In every business, understanding the client base is a must. For this, nothing is more appropriate than opinion mining. If people have negative sentiments or positive emotions, you can find them with ease. The best part, companies can track posts where people have tagged them and their products and view their opinions. Once you start using opinion mining mechanisms, you can perform social media analysis, spot crises, and improve customer services.

Using Opinion Mining to Your Benefit

Sentiment analysis helps you gain insight into your target audience and boosts your company’s sales. If people have negative opinions about your firm or brand, you can track them and solve them in time. There are several areas where opinion mining comes in handy, like the tourism industry, hospital sector, and stock market. You only need to collect a relevant dataset and train your analysis model. Once you train your model, it will provide precise results. Some of you might think that feedbacks and reviews have qualitative data, but with the help of sentiment analysis, you can add quantitative values to your dataset. Individuals will give their descriptive opinions, and your analysis model will assign a sentiment score depending on its emotion.

If your company requires senior analysts, concrete business plans, or sentiment analysts, VizRefra can help you. We have an entire team dedicated to boosting your company sales. Our experts help you visualize analysis results and form adequate strategies. You can connect with us to get further details about our company.