Sentiment analysis of survey responses

sentiment analysis

What is sentiment analysis?

Sentiment analysis uses advanced artificial intelligence technologies like Natural Language Processing (NLP), text analytics, and data science to identify, extract, and study subjective information.

In simpler terms, sentiment analysis classifies text as positive, negative, or neutral.

Traditional metrics, like the number of views, clicks, likes, shares, comments, etc., focus on quantity. The sentiment analysis system goes beyond numbers and focuses on the quality of interactions between the audience and the organization.

Types of Sentiment Analysis

Sentiment Analysis uses different methods to examine the text and determine its emotions. These techniques can be simple, like deciding whether a feeling is positive or negative, or more detailed, offering a deeper understanding of the emotions in the text.

01. Fine-grained Sentiment Analysis

Fine-grained sentiment analysis provides a detailed evaluation of sentiment. It typically includes very positive, positive, neutral, negative, and very negative.

Fine-grained sentiment analysis is useful for customer reviews, where the degree of satisfaction or dissatisfaction is important.

02. Aspect-based Sentiment Analysis

Aspect-based sentiment analysis focuses on identifying sentiment about specific aspects or features of a product or service within the text, such as battery life or camera quality in a phone review.

It can help your business understand which features customers like or dislike.

03. Emotion Detection

Emotion detection goes beyond positive, negative, and neutral sentiments to identify specific emotions expressed in the text. It commonly includes emotions such as happiness, sadness, anger, fear, surprise, and disgust.

It is valuable in social media monitoring to gauge emotional responses to events, campaigns, or brands.

04. Intent Analysis

Intent analysis determines the underlying intention or purpose behind a statement. It includes intents such as inquiry, complaint, suggestion, and compliment.

05. Sarcasm Detection

Sarcasm detection identifies and correctly interprets sarcastic comments, which can often be misclassified as positive or neutral sentiments. It uses advanced natural language processing techniques to detect sarcasm.

Importance of Sentiment Analysis

Sentiment analysis plays a critical role in today's data-driven world. It provides essential insights into public opinion, customer feedback, and market trends. By systematically analyzing the emotional tone of textual data, sentiment analysis offers numerous benefits across various domains.

Here are key reasons why sentiment analysis is important:

How Sentiment Analysis Works?

Sentiment analysis is a detailed process that involves several steps to find, extract, and assess emotions in written text. It uses advanced methods in natural language processing (NLP), text analysis, and machine learning.

Here’s a simple explanation of how sentiment analysis works:

01. Text Preprocessing

Text preprocessing is the initial step where raw text data is cleaned and prepared for analysis.

02. Feature Extraction

In this stage, text is identified and extracted to create a structured format suitable for analysis.

03. Sentiment Classification

This step involves classifying the sentiment of the text using machine learning, deep learning, and sentiment analysis models. The sentiment analysis model learns patterns from the training data to classify new text into predefined sentiment categories.

04. Sentiment Scoring

Sentiment scoring quantifies the sentiment expressed in the text, providing a numerical value that represents the intensity of the sentiment.

05. Post-processing and Visualization

The final stage involves refining the results and presenting them comprehensibly.

Sentiment analysis of survey data

With sentiment analysis, businesses can find out the underlying sentiment from what their customers say about them. Due to its ability to understand text using artificial intelligence and machine learning techniques, sentiment analysis is widely used in market research. Many software gathers “base data” from sources like social media, documents, surveys, etc., and analyzes the emotions. Sentiment analysis tools offer a visual medium to understand feelings and, thus, convert qualitative data into quantitative data.

Sentiment analysis of survey responses is based on two factors:

NLP, rule-based text analysis, and sentiment analysis algorithms process all the input data and output a visual chart, also known as a bubble graph, that classifies different sentiments. It displays positive sentiments in green, neutral sentiments in yellow, and negative sentiments in red.

The bubbles have data filters in the center, which makes it easy for the survey creator to analyze the results. Just by looking, one can quickly identify whether the respondents have had a good or bad experience with their business. The sentiment analytics knowledge graph also shows the percentage of respondents and the kind of experience they had.

sentiment analysis

If the customers are not happy, they express their discontent through feedback forms or customer surveys. A customer satisfaction survey consists of both closed-ended questions, such as multiple-choice questions and open-ended questions. At times, the given answer options are not enough to share experience, opinion, or feedback. In such cases, survey creators use open-ended questions to collect detailed feedback.

The responses to open-ended questions are textual and qualitative. It is impractical to analyze large numbers of responses manually. However, using text analytics and deep learning techniques, it becomes easy to identify the sentiment of textual responses.

If there are too many negative words in survey responses, the businesses can take necessary action to address their concerns. Sentiment analysis of survey responses can help answer questions like:

By analyzing the sentiment of responses to the above questions, businesses can decide if they are heading in the right direction. It also helps measure customer satisfaction levels and reduce the churn rate.

With the QuestionPro platform, you can use the sentiment analytics feature to tag the comments and arrange them as per their sentiment value. You can use data filters to select the terms to be picked up from the responses.

Example of sentiment analysis

Market researchers and PR agencies use sentiment classification and analysis during elections. Data from many sources is collected and analyzed to find out the general current prevalence. They analyze public opinion to understand what people think about leaders.

Just before the elections, political parties, media, consultants, and students conduct several surveys and polls. People share their concerns, needs, and expectations by responding to pre-poll surveys. Parties can predict their chance of winning an election through sentiment analysis and opinion-mining surveys.

For instance, tweets like below express public sentiment and what matters most to them.

“I love what Bernie is doing! Vote Democrat!”

“Key issue in #USelections will be #HealthCare #StopGunViolence”

Based on the social media monitoring analysis and survey responses, parties can formulate their future strategies. Leaders can listen to the voices of people without any filters and act on them.

Many digital marketing and PR agencies use sentiment analysis of Twitter data to measure brand recognition. You can mine all data with #YourBrand and analyze the words used to express emotions and experiences.

Example of sentiment analysis data filters:

Uses of sentiment analysis

Sentiment analysis is useful in many different industries because it helps understand and get insights from text. Here are some important ways it is used:

Advantages of sentiment analysis

Sentiment analysis has many advantages in different fields, such as finding useful information in the text. Here are some main advantages of sentiment analysis:

How to Use QuestionPro Sentiment Analysis Tool?

QuestionPro's Sentiment Analysis feature leverages natural language processing, text analysis, and computational linguistics to systematically identify, extract, and analyze sentiment and emotional tones in survey responses. This tool is widely used to understand customer feedback, improve marketing strategies, and enhance customer service.

1. Accessing Sentiment Analysis

To begin using Sentiment Analysis, you need to access the relevant section in your QuestionPro account.

2. Creating a New Sentiment Report

Creating a new sentiment report allows you to start analyzing the emotional content of your survey responses.

Note: The necessary credits will be deducted from your organization's Global Credits.

3. Viewing and Interpreting the Report

Once the report is generated, you can view and interpret the data to gain insights.

Note: For accuracy, sentiment analysis is performed only on phrases containing 10 or more words.

4. Sharing the Report

Sharing the sentiment analysis report ensures that your colleagues and stakeholders can view the insights.

5. Merging Reports

You can combine multiple sentiment analysis reports to create a comprehensive view of the data.

Note: You can merge up to five reports, and already merged reports cannot be re-merged.

6. Exporting Tagged Data

Exporting the tagged data allows you to download and analyze the information offline.

The exported data includes:

By following these steps, you can effectively utilize QuestionPro's Sentiment Analysis tool to gain valuable insights from survey responses and enhance your data-driven decision-making.

How to use sentiment analysis in your surveys?

Learn how to set up and use this feature with our help file on Sentiment Analysis.

Get started today!

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