Thematic Analysis vs. Sentiment Analysis: Why Thematic Analysis Wins
Have you heard of thematic analysis? If you’re working in insights or data analysis, you’ve likely encountered sentiment analysis. It answers the question, “Is this mentioned in a positive or negative light?”
Sentiment analysis is important because companies want their brand perceived positively, or at least more positively than competitors. If accurate, sentiment analysis can be a valuable tool for this specific use case. However, to truly understand the nuances and context behind customer opinions, businesses turn to thematic analysis of qualitative data.
But what about thematic analysis? It complements sentiment analysis by answering the question, “What are some recurring themes that people mention?” These can range from broad themes like “pricing” to specific ones like “competitors are cheaper.” By conducting a comprehensive analysis of feedback data, businesses can identify common themes and patterns that can drive significant improvements.
The more specific the theme, the more useful it is. And here, I’d like to argue that thematic analysis, qualitative method, always beats sentiment analysis for these six reasons.
But before we begin, what’s the difference between thematic analysis and sentiment analysis?
To analyze qualitative data, researchers often face confusion between thematic analysis and qualitative content analysis. Understanding these differences helps in matching the purpose of the research with the appropriate method of data analysis.
Thematic Analysis vs Sentiment Analysis: A Deeper Dive
While both thematic analysis and sentiment analysis are valuable tools for understanding customer feedback, they serve distinct purposes and offer different types of insights. Understanding their unique strengths and limitations can help businesses choose the right approach for their specific needs.
Feature | Thematic Analysis | Sentiment Analysis |
Focus | Identifies and interprets patterns, themes, and meanings within qualitative data. | Determines the emotional tone (positive, negative, neutral) of text or data. |
Data Type | Works primarily with qualitative data (text, open-ended responses, interviews). | Can work with both qualitative and quantitative data. |
Level of Insight | Provides deep, nuanced insights into the underlying reasons and motivations behind customer feedback. | Offers a broad overview of customer sentiment, but lacks depth and context. |
Output | Generates a list of themes and their frequency, along with supporting quotes and examples. | Produces a sentiment score or polarity (positive, negative, neutral) for a given text or dataset. |
Use Cases | Ideal for exploratory research, understanding customer needs, and uncovering hidden trends. | Suitable for tracking brand reputation, monitoring social media, and gauging overall sentiment. |
Common Challenges | Requires more manual effort and expertise to analyze and interpret data. | Can struggle with sarcasm, irony, and context-dependent language. |
In a Nutshell:
- Thematic analysis provides a deeper understanding of the “why” behind customer feedback, offering rich insights that can drive targeted actions and strategic decision-making.
- Sentiment analysis gives you a quick snapshot of how customers feel about your brand or products, but it doesn’t reveal the specific reasons behind those feelings.
Thematic analysis has many advantages, but it is not without also its limitations.
By combining both approaches, businesses can leverage the strengths of each to gain a comprehensive understanding of their customers, uncovering both the emotional tone and the underlying reasons behind their feedback.
Modern thematic analysis software solutions can include both thematic analysis & sentiment analysis.
1. Thematic Analysis (Qualitative Data Analysis) is More Nuanced
Let's say you learn who's talking about "pricing." Combine this with sentiment analysis, and you learn whether people are happy or unhappy with your pricing. At Thematic, we extract themes that are considerably more specific through our qualitative data analysis.
We can tell you how many people mention themes like "great pricing," "pricing is ok," or "poor pricing." More importantly, we uncover themes like "good value for money," "competitors are cheaper," "price increases," and "deals to new customers."
The more detailed your knowledge from analyzing data, the better decisions you can make. By understanding the specific language and context customers use when discussing your brand or products, you can tailor your messaging and offerings to better meet their needs and expectations.
Thematic
AI-powered thematic analysis + sentiment analysis software to transform qualitative data into business insights
2. Thematic Analysis is More Actionable
Knowing whether something is positive or negative doesn't answer the question "Why?". If you don't know why people like or dislike something, you can't act on it. Consider these two examples:
- "great customer service" vs. "poor customer service"
- "good user interface" vs. "bad user interface"
Great sentiment analysis tools can categorize your feedback in this way. But knowing this raises more questions than it answers.
"What are we doing well, specifically?"
"What do customers expect that we don't deliver?"
These are the questions that thematic analysis can answer, transforming your customer feedback into actionable insights.
Thematic analysis, a qualitative research method, can answer these questions. It can find themes that impact customer service positively, such as "great market knowledge," "answers all my questions," or negatively, such as "long wait time" or "need to call multiple times to resolve an issue."
By identifying common themes, you can monitor your team's performance over time and drive real improvements. When it comes to themes related to user interface, unexpected themes like "cannot find your phone number" can uncover valuable unknown unknowns through a comprehensive analysis of qualitative data.
When conducting qualitative data analysis of textual data, such as open-ended survey responses or social media posts, don't just skim for keywords. Immerse yourself in the language and context. Qualitative research involves identifying not only what people say but how they say it. The tone, specific phrases, and underlying emotions can reveal valuable insights that go far beyond simple keyword counts.
Remember, the goal of thematic analysis is to uncover the deeper meaning and recurring patterns within the data. By paying close attention to the nuances of language, you can identify emerging themes, unmet needs, and hidden opportunities that might otherwise be missed. This deeper understanding can lead to more targeted and effective decision-making, ultimately improving customer satisfaction and driving business growth.
3. Thematic Analysis is More Accurate
Thematic analysis can be more accurate because it can capture themes that sentiment analysis easily misses. A simple sentiment analysis lacks common sense. It's incredibly hard to teach computers common sense, which tells us that "slow" is positive in the context of "slowing down for passengers" but negative in "slow loading time."
Sentiment analysis also fails regarding specific actions, whereas thematic analysis captures them. For example, in responses to a student survey, Thematic found themes like "spread exam days evenly," "more practical courses," or "student registration admin."
These actionable insights would be missed by sentiment analysis alone. Thematic analysis provides a more accurate and comprehensive understanding of the feedback data, leading to more informed decision making.
4. Uncovering Hidden Trends in Qualitative Data
Thematic analysis excels at uncovering hidden trends within qualitative data that sentiment analysis might miss. By analyzing large volumes of textual data, such as open-ended survey responses, social media posts, or interview transcripts, thematic analysis can reveal emerging issues, unmet needs, or shifting customer preferences.
For instance, a thematic analysis of customer feedback might reveal a growing trend of customers desiring more sustainable product options, even if this sentiment isn't explicitly expressed as positive or negative.
Identifying such trends allows companies to be proactive, adapting their strategies and offerings to meet evolving customer demands, giving them a competitive advantage in their industry.
5. Flexibility and Adaptability
Another advantage of thematic analysis is its flexibility in adapting to different types of feedback and evolving themes over time.
Unlike sentiment analysis, which often struggles with nuance and context, thematic analysis can handle diverse feedback sources, including open-ended questions, customer surveys, and social media conversations.
As customer sentiments change, thematic analysis can easily adapt, allowing companies to stay ahead of emerging trends and maintain a deep understanding of their customers. This flexibility makes thematic analysis a valuable tool for ongoing customer feedback analysis.
During the coding process of thematic analysis, don't solely rely on existing theories or predetermined categories. While these can be helpful starting points, keep an open mind to emerging themes that may not align with your initial expectations.
Pay close attention to the sentiment expressed in the data, but also look beyond simple positive or negative labels. Explore the nuances of how people articulate their thoughts and feelings. When analyzing large volumes of data, consider using software tools to help identify potential themes, but always validate and refine these with your own careful review.
The most valuable insights often come from unexpected connections and patterns that emerge from the data itself. By remaining flexible and open to new possibilities, you can ensure that your thematic analysis captures the full richness and complexity of your qualitative data.
6. Better Insights for Decision Making
Thematic analysis translates into more actionable insights by identifying specific themes and areas for improvement. While sentiment analysis might indicate a general trend of positive or negative sentiment, thematic analysis provides detailed, context-rich insights that can directly inform data-driven decisions.
For example, if thematic analysis reveals a common theme of customers expressing frustration with a particular product feature, companies can take targeted action to address this issue, leading to improved customer satisfaction and loyalty.
This qualitative research method goes beyond surface-level sentiment to provide a deep understanding of customer needs and motivations.
In Summary
In summary, without thematic analysis, sentiment analysis isn't as actionable as it can be and can even lack accuracy. Thematic analysis goes beyond simply categorizing feedback as positive or negative.
It delves deeper into qualitative data to uncover the reasons behind customer sentiments, identify emerging trends, and provide meaningful insights that drive strategic decision-making. By combining thematic analysis with sentiment analysis, businesses can gain a comprehensive understanding of their customers and make informed decisions that enhance customer satisfaction and drive business growth.
So, if you need to analyze people's feedback accurately, make sure you're discovering emerging specific themes. After seeing how powerful thematic analysis can be for delivering insights from customer surveys, we decided to name our company after it, but it's not just thematic analysis that we do now.
Book a free guided trial of Thematic on your own data, to see how we can get the most out of your customers' feedback.