Your Sort Guide to Sentiment Analysis

Sentiment analysis — the process of examining texts for opinions and feelings with Natural Language Processing (NLP) and Machine Learning (ML) — gains in popularity with more and more applications appearing to unleash its potential. From providing businesses with insights on how people feel about certain products, topics or advertisements to identifying signs of depression in mental health apps and detecting positive or negative opinions within a text — sentiment analysis covers a broad range of needs across various domains.

For example, at Elinext, we successfully developed a platform that allows sentiment analysis of tweets and in this way provides a Polish analytical agency with the opportunity to receive valuable information on public attitude towards Polish political parties, their leaders or players, their speeches and events right before the elections. Our solution allows finding out which actions or words shape the public opinion, as well as seeing which words or phrases used by Twitter users are linked to some party or its player. 

So now let’s have a deeper look at how sentiment analysis works, its real-life use cases with benefits and key challenges.  


The Main Working Principles of Sentiment Analysis

Let’s start with some basics to understand how sentiment analysis works. There are three types of NLP algorithms used for sentiment analysis. These are:

  • Rule-based — takes advantage of manually crafted rules. 
  • Automatic — is based on ML techniques.
  • Hybrid — is a combination of the two above.

Sentiment analysis studies subjective information in an expression. The most common sentiment analysis (SA) models are:

  • Fine-grained SA — is generally represented by 5-star ratings that allow detecting polarity categories, such as positive, neutral, negative, etc.
  • Aspect-based SA — determines the polarity of words that are used to express the attitude towards the keyword ( this could be some feature or aspect of a product). 
  • Emotion detection — use lexicons and complex machine learning algorithms to detect emotions like happiness, frustration, anger, sadness, etc.

How Sentiment Analysis Can be Used?

It’s estimated that 80% of the world’s data is unstructured, and sentiment analysis helps businesses make sense of all data presented in text format. It is especially useful for analyzing data on social media platforms, as well as for marketing purposes, product and customer support. Below, we provide some examples of how it can help:

Sentiment Analysis in Social Media Platforms

Today, social media platforms have become one of the richest sources of information, especially when it comes to feedbacks and opinion sharing. Not taking advantage of such data would be a huge mistake, so how can businesses do it? 

With sentiment analysis, it becomes possible to receive deep insights into what people really think about products, services, or any other thing of interest. The insights come in various forms, from positive or negative assessment to determination of certain words that ‘stick’ to the keyword (this could be a brand name, businessman, team member, whatever). 

Sentiment analysis in social media also allows segmenting audience by some criteria and receiving insights on how each of these segments feels about a certain product, service, etc. It also serves as a means for automatic detection of mentions and provides the ability to route them to team members for the response. 

Key benefits of sentiment analysis in social media allow:

  • Filtering mentions and to prioritize actions
  • Keeping an eye on trends
  • Taking a look back at the moments when things got better or worse
  • Taking a competitive advantage on the market

Brand Monitoring with Sentiment Analysis

In addition to social media monitoring, brands can consider finding mentions all across the web — on websites with articles, blogs, forum discussions, etc. With sentiment analysis, it is possible to collect all of these mentions with information on their quality (positive or negative). In this way, a brand can estimate whether there is a need for some urgent measures and then act accordingly. And one of the best things is that such analysis can be automated, so alerts will come to team members in a short time after they appear somewhere on the web. In other words, sentiment analysis for brand monitoring allows:

  • Getting immediate information on customer sentiment
  • Providing additional protection to brand reputation
  • Keeping a birds-eye of competitors and factors that impact their reputation
  • Knowing exactly when it is the best time to take actions to avoid potential crises
  • Prioritizing actions 
  • Tuning into a specific point in time

Analyzing Sentiments in Customer Feedback

Customer support interactions also can become a rich and valuable source of information on what improvements could be done to a business performance on the market. It is a common situation when companies ask their customers if they would recommend this company, product, and/or service to a friend or family member to identify the number of promoters, passives, and detractors. With sentiment analysis, any customer feedbacks can be classified as positive or negative, as well as to track their sentiment about some specific aspects of a brand operation, giving the company a precious opportunity to elevate more customers to the “promoter” level. In short, applying sentiment analysis to customer feedbacks allows businesses to:

  • Create more precise questions for customers
  • Get a better understanding of customer experience
  • See how it changes over time and what factors impact such changes
  • Separating customers into segments by some criteria
  • Responding to arising problems in the most effective manner

Sentiment Analysis for Improvement of Customer Service

According to McKinsey & Company, 25% of customers will switch to dealing with your competitors right after the first negative experience with your company. To decrease the number of such situations, sentiment analysis can be applied to automate classification of incoming queries and in this way provide better and quicker services, to detect customers previously classified as detractors to make their tickets prior and automatically redirect them to specific team members, as well as to get a deeper understanding of how your customer support works and what improvements could be done. In short, with sentiment analysis applied to customer services, companies can:

  • Effectively prioritize tickets
  • Build a stronger customer support strategy
  • Efficiently address to urgent matters

Sentiment Analysis for Market Research and Competitive Analysis

To keep their businesses up to date, companies always pay a lot of attention to market research that allows them identify future trends and gain a competitive advantage. With sentiment analysis applied to market research, businesses can carefully analyze product reviews to draw conclusions, better target their products and services, take advantage of comprehensive reports, and understand what customers really think about their products by analyzing texts with company mentions whenever they are posted. Key benefits of using sentiment analysis in market research include:

  • Finding new, meaningful sources of information
  • Getting valuable insights on company image and competitors’ performance
  • Replacing retrospective approach with the one that uses real-time information
  • Automating reports (monthly, weekly, daily)

Key Challenges

Despite sentiment analysis offers a wide range of benefits, it is still not perfect. Analyzing texts for sentiments is a difficult task even for a human. And when it comes to machines, the major challenges for today include: 

  • Detection of subjectivity in texts
  • Understanding changes in polarity depending on the context
  • Identification of irony and sarcasm
  • Classification of comparisons as positive, negative or neutral
  • Detection and analysis of Easter, Western, and other Emojis and Smiles
  • Setting up parameters for neutral words and expressions

As you can see, sentiment analysis is not precise as it hard for machines to detect nuances in human language.  

So Is It Worth of Being Implemented?

The short answer to this question is: for sure, it is worth your efforts. Although sometimes machines can make mistakes in their predictions, such situations are quite rare. The analysis of the biggest part of text information (about 70-80%) will provide your business with meaningful, valuable information on what your customers think about your products, services, brand, activity, ideas, etc. What is more, it will save a lot of time on report creation, automate certain tasks, increase your productivity, improve performance, and save your employees from frustrations.  

If you have any questions, we are always here to help you with finding answers. Feel free to contact us anytime, and thank you for reading. 

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