Sentiment analysis

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.

This type of analysis usually takes texts such as comments and written reviews as a starting point to determine the tone of the messages, classifying them as negative, neutral, or positive.

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.

These techniques often generate ways to classify different types of sentiment analysis.

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.

• Sentiment analysis helps businesses understand customer feelings about their products, services, or brands.

• It helps businesses enhance their customer service strategies.

• Sentiment analysis provides valuable insights into market trends and competitor performance.

• Businesses can leverage sentiment analysis to refine their products and services.

• Sentiment analysis is crucial for real-time monitoring and response due to the vast amount of data generated on social media platforms.

• Sentiment analysis can improve patient care and support clinical decision-making.

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.

• Tokenization: Splits the text into individual words or tokens.

• Stop Word Removal: Removes common words that do not contribute much meaning (e.g., "and," "the").

• Lemmatization/Stemming: Reduces words to their base or root form (e.g., "running" becomes "run").

• Punctuation Removal: Eliminates punctuation marks to simplify the text.

02. Feature Extraction

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

• Bag of Words (BoW) represents text as a collection of words and their frequencies.

• Term Frequency-Inverse Document Frequency (TF-IDF) weighs the importance of a word based on its frequency in a document relative to its frequency in the entire dataset.

• Word Embeddings utilizes AI models to convert words into numerical vectors that capture semantic meaning.

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.

• Polarity Scores: It assign scores indicating positive, negative, or neutral sentiments.

• Emotion Scores: It scores specific emotions like joy, anger, sadness, etc.

05. Post-processing and Visualization

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

• Aggregation: Summarizes individual sentiment scores to provide an overall sentiment for larger text datasets.

• Visualization: Uses charts, graphs, and dashboards to present sentiment analysis results for easy interpretation.

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:

• 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 identify whether a customer is a 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 by analyzing the sentiment in their responses. For instance, airlines can use a sentiment analysis tool to gather passengers' feedback and find areas for improvement. Say, some of the respondents commented, “We had a great journey, but the 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, or else the customer might switch to a competitor airline.

• Competitor analysis: You can also use sentiment analysis to determine what people think about your competitors. 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.

• 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 is less chance 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 their relationships with customers. It closes the feedback loop quickly and adds dynamism to the data collection and action process.

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, posts 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:

• Examples of positive sentiment: Good, like, excellent, and 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.

Uses of sentiment analysis

Sentiment analysis is useful in many different industries because it helps understand and get insights from text. Here are some of the most common uses where this type of analysis is typically applied:

• 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 events 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 improve their experience.

• Reputation management: PR agencies use sentiment analysis tools to manage their clients' reputations 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 address can adversely affect a company's reputation.

• 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 question 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.

Turning Insights into Actions in Sentiment Analysis Using AI

AI sentiment analysis is changing the way businesses work with data. With technologies like NLP and machine learning, companies can analyse huge volumes of text data and turn insights into action. Here’s how:

1. Enhancing Customer Support and Personalization

AI can find emotions in customer feedback from reviews, support tickets or social media. So businesses can respond better:

• Highlight customer pain points for faster resolution.

• AI chatbots respond with answers that match customer emotions.

This means better support and stronger customer relationships.

2. Improving Products and Services

Sentiment analysis shows what customers like or dislike about a product. For example:

• Users love the app’s design but hate the speed.

• Developers can fix the most important issues to improve user satisfaction.

This improves products and loyalty.

3. Staying Ahead in the Market

AI lets businesses monitor sentiment across social media, forums and news sites. With RNNs and LSTM models companies can:

• Pick up on customer shifts early.

• Align offerings to market demand to stay competitive.

This helps businesses stay relevant.

4. Building a Strong Brand

Customer perception is key to a successful brand. AI sentiment analysis helps by:

• Analyze reviews, surveys and conversations to get the full picture.

• Make sure brand messaging lands with the target audience.

This closes the gap between perception and reality and builds stronger customer relationships.

5. Improving Employee Satisfaction

AI isn’t just for customer feedback, it’s also good for employees. By analysing survey responses and performance reviews businesses can:

• Identify employee dissatisfaction.

• Make changes to boost morale and productivity.

This balances customer and employee satisfaction.

AI sentiment analysis is all about turning insights into action. It helps businesses deliver better customer experiences, products and innovation. By putting these insights into practice businesses can grow, stay ahead and build for the long term.

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:

• Subjectivity: Personal feelings, opinions, or experiences that are subject to change from person to person.

• Degree: The extent or range of emotions from positive to negative.

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.

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:

• 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 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 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.

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.

• Log in to your QuestionPro account.

• Navigate to your survey.

• Go to the Analytics section.

• Click on the Text Analysis icon.

• Select Sentiment Analysis from the drop-down menu.

2. Creating a New Sentiment Report

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

• Click on the "+ New Sentiment Report" button.

• Enter a name for your report.

• Choose the relevant survey question from the dropdown menu.

• Optionally, apply a data filter if you want to filter the responses.

• Click on "Use Credits and Generate Report" to create the report.

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.

• Find the generated report in the list of reports.

• Click on the report name to open it.

• View the bubble chart displaying the top 10 tags for the selected question.

• Click on a tag within the chart to explore the root cause of the issue.

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.

• A shareable link for the report is available at the top right section of the report.

• Use this link to share the report with colleagues or stakeholders.

5. Merging Reports

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

• Select the reports you want to merge.

• Click on the "Merge Reports" link.

• Enter a name for the merged report.

• Click on the "Merge Reports" button.

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.

• Click on the Excel Sheet icon on the top right corner to download the tagged data.

The exported data includes:

• Theme: Tags/themes along with Response Count, Sentiment, and Sentiment Score.

• Raw Data: Response ID, response for the selected question, Theme, Sentiment, and Sentiment Score for each tagged response.

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.