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:
Subjectivity: Personal feelings, opinions, or experiences which are subject to change from person-to-person.
Degree: The extent or range of emotions from positive to negative.
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.
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,
What do our customers like about our products and services?
What do our customers don’t like about our products and services?
Are we getting too many negative responses recently?
Has the number of negative responses increased gradually?
Which brand product has the highest number of positive responses?
Has the number of positive, neutral, and negative responses remained constant as compared to last quarter?
Is there a shift in the degree of positive or negative responses?
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.
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:
Examples of positive sentiment: Good, like, excellent, recommend.
Examples of neutral sentiment: Can’t say, don’t know, maybe.
Examples of negative sentiment: Disappointed, needs improvement, didn’t like, won’t recommend.
Surveys: Sentiment analysis in the voice of customer surveys to understand reviews, suggestions, concerns, and complaints. Based on text analytics, sentiment analysis tools classify responses as positive, neutral, or negative sentiments.
Analysis of data from social media sites: People often react to any event or news articles on social media sites. Their posts can be used to understand their reactions to events or the buzz that specific marketing campaigns have created. Based on the analysis, create a strategy to better reach out to people and change their experience for the better.
Reputation management: PR agencies use sentiment analysis tools to manage the reputation of their clients on public platforms. Once you identify negative comments, you can quickly respond to your followers and keep the situation in control. The absence of quick identification and addressal can adversely affect the reputation of a company.
Personalized marketing: Marketing based on online sentiment analysis can be used to offer personalized products, services, and discounts to target audiences and improve the chances of conversion.
Forecast sales: Sentiment analysis of open-ended questions data can aid in forecasting sales and creating a future strategy.Based on the feedback, organizations can find the likelihood of existing customers renewing their contracts.
Quantitative analysis of qualitative data: It’s difficult to capture emotions, feelings, or sentiments from advanced question types like semantic differential scale, side by side matrix, flex matrix, etc. Also, the net promoter score helps in identifying whether a customer is promoter, passive, or detractor. However, it doesn’t dig deep or gather the reasons behind their experience. Sentiment analysis of customers’ comments can help understand the ‘why’ behind their responses.
Customer experience measurement: Understanding customer experience becomes easier with analyzing the sentiment in their responses. For instance, airlines can use a sentiment analysis tool to gather the feedback of passengers and find out improvement areas. Say, some of the respondents comment, “We had a great journey, but food could have been better”. It implies that they had a good experience overall, but expect better food. Hence to retain existing customers, they’ll need to improve their food service, else the customer might switch to a competitor airline.
Competitor analysis: You can use sentiment analysis to dig into what people think about your competitors as well. It’s this competitive research that allows you to reevaluate your priorities and stay one step ahead of the competition.
Identification of trends: Sentimental analysis lets you find out early signs of a positive or negative event before it happens. These flags can help the management team to plan their future course of action. If the number of negative reviews is gradually increasing, companies can take corrective measures before it gets worse.
Time-effective: Instead of making sense out of complex numerical reports, sentiment analysis presents data in bubble graphs. Looking at visual charts saves a lot of time as compared to statistical reports. It also displays the sentiment score of different key terms and an option to drill down on a tag. You can also check the proportion of a specific tag in the entire data set.
Team collaboration: Online sentiment analysis tools can export text and sentiment analysis reports to standard formats like Excel and SPSS. You can share these reports with other teams to collaborate and work together.
Consistent standards: Emotions are subjective and vary from person-to-person. Transforming emotions in the feedback surveys into numbers and graphs brings consistency to the table. Hence, there are lesser chances of disagreement within the team and throughout all the market research projects.
Real-time analysis: Sentiment analysis of data in real-time allows decision-makers to act quickly and improve the relationship with customers. It closes the feedback loop soon and adds dynamism to the process of data collection and action.
Learn how to set up and use this feature with our help file on Sentiment Analysis.