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What Is The Role Of Data Science In Sentiment Analysis?

Sentiment Analysis
Published on Oct 28, 2020

With multiple channels available on social media, it has become extremely easy for customers to communicate their feelings and requirements efficiently. Hence, for brands to understand these social cues and cater to customer requirements has become imperative. In fact, according to a study conducted by Forbes, the number of professionals making use of big data and predictive analytics grew from 40% in the previous year to 60% in 2019.

Essentially, all brands need to do is carry out marketing analytics services like listening and understanding what customers are saying on various online platforms and tailor their offerings based on the issues and requirements they identified.

However, identifying customer requirements amongst hordes of data is no easy feat. You need advanced data science services like a systematic technique and methodology to cut through the noise and be able to derive insights that add value. Hence, sentimental analysis has been gaining a lot of traction and popularity over the years.

In this article, we will discuss what is sentimental analysis in-depth and understand the role of data in this technique. So, let’s get started with the basics.

What Is Sentiment Analysis?

The primary objective of sentiment analysis is to assist businesses in understanding the general sentiment of their brand, product or service among customers by monitoring conversations posted online. It essentially conducts contextual mining of text, through which personal information is extracted from the source material.

In the past, sentiment analysis was restricted to count-based metrics and information accumulated from social streams. This wasn’t proving extremely beneficial as it was just scratching the surface and completing missing the insights that actually added value.

However, with the introduction of recent technologies like Machine Learning and AI, sentiment analysis has become an extremely essential and effective tool for conducting in-depth research and deriving high-value insights. This is because the said technique enables brands to study a customer’s conversation and derive important insights, including:

  • The key aspects of the product or service that the customers actually care about
  • The underlying intentions of the user and their reaction to those particular aspects

This information is highly crucial for brands as they can make adjustments and refine their products and services to match the customers’ requirements with the help of such data.

Sentiment Analysis Types

Sentiment analysis primarily takes two factors into consideration; polarity, i.e., positive, negative, neutral, and emotions like happy, angry and sad. It may also focus on intentions like interested vs not interested.

In the section below, we’ve listed some of the most popular types of sentiment analysis:

Fine-Grained Sentiment Analysis

When businesses are trying to identify what their customers are looking for, polarity is extremely crucial. Hence, you can use sentimental analysis to identify a target audience’s reaction in categories like:

  1. Very Positive
  2. Positive
  3. Neutral
  4. Negative
  5. Very Negative

To better understand each category, most companies ask their customers to provide their feedback in the form of star-based rating. A good example of this is:

  • Very positive = 5 start
  • Very negative = 1 star

Emotion Analysis

As the name suggests, this type of analysis is aimed at identifying emotions like happiness, sadness, anger, frustration, and others. To identify these emotions, the most common technique used is lexicons, which are a list of words that resonate with the emotions that are conveyed.

While lexicons are useful, they also have a couple of downsides, primarily as people tend to use the same words to express different emotions. For example, kill is generally used in a negative manner like, “your customer support is killing me.” On the other hand, kill can also be used in a positive manner like, “I love your product; you are killing it.” Hence, you need to be careful while using this type of analysis.

Data Science in Sentiment Analysis

Aspect-based Sentiment Analysis

This type of analysis is particularly useful for product reviews as it allows you to identify exactly what parts of your service or product the customer likes and doesn’t like. For example, for a review that says, “the camera is great, but the battery life is too short,” you’ll be able to determine that the camera works great, but the battery needs some work.

So, essentially, aspect-based analysis helps you determine how your customers feel about your product, i.e., good, bad, neutral.

Multilingual Sentiment Analysis

This is one of the most difficult types of sentiment analysis, but if done right, the results are extremely rewarding. To conduct multilingual analysis, you need preprocessing and resources like Lexicons. Moreover, you’ll also need to create algorithms like translate corpora and noise detection.

To make the process easier, you could also use MonkeyLearn’s language classifier that detects the language in texts automatically. Once you’ve set this up, you’ll need to build a custom model to classify text that is in the language that matches your requirement.

The Need Of Sentiment Analysis To Understand Data

A latest study conducted by IDC reported that over 80% of the world’s data will be unstructured by 2025. This essentially means that while there is a large amount of data being created on a daily basis, including emails, social media conversations, support tickets, surveys and articles, it’s extremely difficult to understand and analyse this data. Not only is it time-consuming, but extremely expensive as well.

With an aim to make sense of this unstructured data by analysing, processing and sorting it, sentiment analysis is a boon for businesses. We’ve listed the main benefits of using sentiment analysis below.

Scaleable Sorting of Data

There is a plethora of information and data available for businesses online in the form of tweets or comments, customer support conversations and even surveys. Now, imagine sorting through all of this data manually. The amount of time and effort required to accomplish this is unreasonable.

However, with sentiment analysis, you process a large amount of data in a cost-effective and timely manner.

Real-Time Analysis

Sentiment analysis is a great tool for resolving issues in real-time. By helping you identify a troubling situation before it escalates, you can take immediate action to resolve the issue before it causes greater damage. For example, a PR crisis on social media can be tackled before it is blown out of proportion, or an angry customer can be pacified before they tarnish your brand’s name and image.

Maintain The Criteria

A study conducted by MonkeyLearn stated that only approximately 60-65% of people agree to a particular sentiment based on the text given. For example, a post that says, “it’s an okay product” could be either perceived as positive or negative. When text is checked manually, the sentiment is highly influenced by the personal experiences, beliefs and thoughts of the person.

However, by applying the sentiment analysis technique, companies are able to create a system that uses the same criteria while analysing data, which can considerably help increase the accuracy of the insights derived.


Sentiment analysis is extremely important, especially as it can be applied to a host of various industries and areas of business, including brand monitoring and customer support to product analysis and market research. This helps businesses derive new insights while empowering teams to work faster and with more accuracy. Due to this, sentiment analysis is no longer just another trend or buzzword that will lose its prominence over time. Instead, it has already become a crucial tool for businesses and will surely continue to grow! To leverage from this tool, all businesses need is data analytics consulting services for high-quality, authentic and quality data.