Sentiment analysis can shed more light on these topics and become a helpful tool to analyze the moods and opinions of your clients. With the advent of social networks and digital marketing, customers’ opinions about products and brands have become increasingly visible. User feedback online, such as reviews, social media comments, and surveys, contains tons of valuable data. This information may provide insight into what customers think about your product, what they like and dislike, and, most importantly, how to react to their feedback.

This article will focus on analysis and its importance for online-based businesses, its main approaches, and the role of machine learning (ML) and natural language processing (NLP) in it.

Also Read: Types of Quality Inspection Services


Sentiment analysis explained

Sentiment analysis, also often referred to as opinion mining, is an automated method used to identify, extract, quantify, and research attitudes and opinions towards a brand, product, or service. This method relies on NLP, computational linguistics, machine learning, and other tools. It helps allocate sentiment scores to the entities within a written sentence and determine positive, negative, or neutral sentiment in the text.

This automated method allows businesses to analyze a large number of customer reviews and social media data to understand how customers feel about the brand and its products, whether they are satisfied with pricing conditions and customer service. This way, brands can gauge public opinion, conduct detailed market research and review monitoring. All these measures, in turn, help businesses adjust to their customers’ needs and tailor their products correspondingly.

Types of sentiment analysis 

Sentiment analysis models aim at defining polarity (positive, neutral, negative), emotions (disappointed, happy, furious), intentions (interested or not, willing to buy or not), and urgency. Depending on your analysis goals, you can use various categories to interpret customer feedback and adjust them to your specific needs. Some of the most popular analysis types include:

Fine-grained sentiment analysis

If you seek to make your sentiment as precise as possible, you can add additional polarity categories, such as:

  • Very negative
  • Negative
  • Neutral
  • Positive
  • Very positive 

These categories correlate with five-star rating reviews, where very positive is equal to 5 stars and very negative is equivalent to 1 star.

Emotion detection 

This type focuses on emotions and feelings, e.g., frustration, happiness, and others. Many of the emotion detection approaches are lexicon-based, meaning they use systems of emotionally charged words. You can also use machine learning algorithms to detect the sentiment behind certain words.

Aspect-based sentiment analysis

When analyzing sentiments in a piece of text, brands want to know what specific features and aspects of their products customers are discussing in a positive, negative or neutral way. For example, in this review: “The camera in this phone is worse than I expected,” a negative opinion is expressed towards a particular feature of the product.

Why is sentiment analysis important? 

Since sentiment uses automated methods, it makes it possible to sort out and analyze enormous amounts of the sentiment behind social media conversations and reviews in a timely manner. As a result, companies can make better and more informed decisions based on sufficient data and in-depth analysis.

Overall, basic sentiment facilitates the process of gathering and measuring social data in several ways:

  • Seizing large amounts of data.According to the World Economic Forum, it was expected that the amount of data online was going to reach 44 zettabytes by 2020, which is 40 times more bytes than the stars in the observable universe. These statistics are both stunning and intimidating since there’s no way to collect and process this data manually. Therefore, you would need automated analysis tools.
  • Real-time analysis. It is always crucial to stay updated on your customers’ opinions and reactions in real time to take action immediately if a severe problem arises.  
  • Centralized analysis criteria. Deciding on whether a piece of text is positive, neutral, or negative can be a challenging task for humans since they may make subjective judgments based on their previous experiences and beliefs. That is why it is better to be guided by a unified analysis system that can be applied to all text data.

How does sentiment analysis work?

To understand how sentiment works, we need to dig deeper into the main approaches it employs. There are three major analysis algorithms that can be implemented in analysis and opinion mining: rule-based (lexicon-based), automatic (machine learning), and hybrid.

Rule-based approach

Most of the time, rule-based analysis algorithms rely on manually crafted rules to determine polarity, subjectivity, and sentiment in a piece of text. These rules are based on different NLP analysis techniques that were initially developed in computational linguistics, including part-of-speech tagging, tokenization, stemming, etc.

In this approach, sentiment analysis makes use of analysis datasets, e.g., large libraries of adjectives (good, fantastic, disgusting, terrible) and phrases (excellent service, awful movie) that have been previously assigned particular scores by human coders.

This hand-scoring process can be tricky and inaccurate since everyone participating in it has to come to an agreement regarding the sentiment scores. For instance, if one person assigns a sentiment score of 0.5 to the word good, but another person gives the same sentiment score to the word amazing, your analysis system will perceive both words as equally positive, which will lead to subsequent confusion and wrong results. 

Let’s take a look at an example of how a rule-based analysis system works:

  1. Determines two polarities with two lists of polarized and sentiment-bearing words, e.g., negative words such as horriblebadawful, and positive mentions such as bestgoodfabulous, etc.
  2. Attaches a sentiment score to each word and component.
  3. Counts how many times positive and negative words appear in the text. 
  4. If the number of negative words is bigger than the number of positive words, the system returns a negative sentiment and vice versa. If the numbers are equal, the total sentiment will be marked as neutral.

The rule-based algorithm is easy to implement and clear in terms of the rules guiding the analysis; however, it’s too simplified and not capable of dealing with more complex word combinations. This algorithm needs additional rules to make it more accurate, which requires constant investment to maintain development.

Sentiment analysis challenges

Analysis is one of the most challenging jobs in NLP since even people may struggle to identify and analyze sentiment correctly. Even though analysis models are getting more superior and accurate, there are still numerous obstacles that prevent them from being the ultimate solution.


All spoken and written words are uttered in some specific circumstances, at some point in time, by some particular people and to other people. In other words, they all have context behind them. The problem is that machines cannot recognize the context if it isn’t brought up on purpose. Let’s imagine a situation where we have two responses to a survey regarding a recent conference:

  1. All of it.
  2. Totally nothing.


As customers generate more and more reviews and comments online daily, it’s evident how important it is to process this data and draw conclusions promptly. Sentiment analysis provides an understanding of how your clients feel about your brand and product and how you can improve your services. Based on natural language processing and constantly progressing machine learning techniques, analysis serves multiple use cases, including brand monitoring and market research.

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