How to Use Sentiment and Text Analytics to Collect Data

Chi Whitley

For businesses, wouldn’t it be wonderful to have both qualitative and quantitative data that signifies what customers think about a brand, their product(s), and overall services? Well, with sentiment analysis tools, businesses can collect key data and insights from their customers’ surveys response, online reviews, and social media interactions.


What is Sentiment Analysis

Sentiment analysis is a type of machine learning that uses language processing techniques, text analytics, and linguistic computational analysis to determine a person’s overall sentiment. Typically, this sentiment is broken down into three categories: positive, negative, or neutral. 


Fine-Grained Sentiment Analysis of Smartphone Review


For example, if a customer left a Google review that stated, “I love the product, but my interaction with John Doe during the demo was adequate.” A sentiment analysis tool would likely rate this into two categories:


  • Positive for the product due to the “I love the product.”
  • Neutral for the customer experience, “John Doe…adequate.”


1. Sentiment Analysis Types

There are four main types of sentiment analysis. Sentiment analysis typically focuses on three categories:

  • Polarity (positive, negative, neutral) 
  • Emotions (irritation, happiness, relief)
  • Intention (interest level, purchase, or pass)

The types of sentiment analysis below should be used to measure the different categories. 

2. Fine-Grained Sentiment Analysis

Our first example was fine-grained sentiment analysis. The fine-grained analysis examines a customer’s polarity and measures their response or language as a neutral, positive, or negative sentiment. If more granular feedback is needed in a survey, consider expanding the Likert scale to great, good, neutral, bad, very bad.

3. Emotion Detection

Emotion detection sentiment analysis is a more high-level form of text analytics. Emotion detection detects emotions such as anger, frustration, remorse, happiness, or irritation. To successfully detect these emotions, the text analytics tool uses a lexicon (list of words and their corresponding emotions) and complex machine learning algorithms to gain key insight and to detect the correct emotions. 

From Sentiment Analysis to Emotion Recognition: A NLP story | by Rodrigo Masaru Ohashi | Neuronio | Medium

4. Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis gets commonly used when analyzing product reviews. It’s best to learn how customers feel about a specific aspect or feature of a product. For example, an aspect-based classifier could determine that this customer’s comment, “the software’s load time was unbearably slow” signifies a negative and frustrated opinion about the load time. It’s accurate and specific.

Intent Analysis

The intent analysis looks to recognize the customer’s overall objective or goals. For example, intent analysis help conclude if a customer’s message, comment, or review is a complaint, suggestion, query, or simply gratitude. 

Sentiment Analysis and Alternative Data

Sentiment and text analysis tools are quite useful for businesses looking to turn loads of alternative data into predictive insights

For instance, if multiple customers comment on social media or in online forums that they’re in dire need of a new product update, a business can put all efforts towards this new update — not just software developers, but the marketing, social media, CX, and PR team. 

The PR, social media, and marketing departments can help build suspense through marketing and PR strategies, while the CX team can keep customers satisfied during different stages of the customer journey as the product rollout approaches. 

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