Sentiment analysis of survey responses

image 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. Sentiment analysis goes beyond numbers and focuses on the quality of interactions between the audience and the organization.

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 gather “base data” from sources like social media, documents, surveys, etc. and analyze the emotions. Sentiment analysis tools offer a visual medium to understand the feelings and, thus, convert qualitative data into quantitative data.

Sentiment analysis of survey responses is based on two factors:

NLP and rule-based text analysis algorithms process all the input data and outputs 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 make it easy for the survey creator to analyze the results. Just by having a look, one can quickly identify if the respondents have a good or bad experience with their business. The sentiment analytics knowledge graph also shows the percentage of the respondents along with the kind of their experience.

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 responses in large numbers by going through them manually. But with the use of 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 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 prevalent. 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 by doing sentiment analysis and opinion mining of 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 analysis of social media monitoring and survey responses, parties can formulate their future strategies. Leaders can listen to the voice 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

Advantages of sentiment analysis

How to use sentiment analysis in your surveys?

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

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