Thematic coding and sentiment analysis - what's the difference? In this blog post, we'll examine when to use each method and explain why combining the two can yield the best results.
Understanding Sentiment Analysis and Thematic Coding
Sentiment analysis and thematic coding are two methods of qualitative data analysis that are (rightly) often grouped together. Sentiment analysis determines whether a text's meaning is positive, negative, or neutral. Thematic coding connects patterns to uncover deeper insights that can be missed in sentiment analysis.
What is Sentiment Analysis?
Sentiment analysis is all about assessing the mood behind customer feedback. It is particularly useful on unstructured datasets like online surveys, social media mentions, or chat conversations. It provides a high-level summary of customers' experiences, and—thanks to advancements in AI—it is highly scalable, meaning it can analyze vast amounts of data in a matter of minutes. It's business uses include;
- brand monitoring
- competitive research
- employee engagement
- product analysis
Sentiment analysis also has its limitations. Although AI is improving its accuracy, the many quirks and intricacies of human language mean there are always going to be errors, particularly when it comes to detecting sarcasm or words with multiple meanings. However, the greatest limitation of sentiment analysis is its simplicity. It is solely about understanding whether the meaning of the text was positive, negative, or neutral—rather than the factors that have led to these feelings.
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What is Thematic Coding?
Thematic coding (or thematic analysis) is the process of analyzing words and sentences to detect themes in the text. It is entirely separate from sentiment analysis in that two pieces of text can fall under the same theme but have different sentiments. For example, restaurants will often receive feedback on customer service, for better or worse. If one person writes a review raving about exceptional service and another shares frustration about their order taking too long to arrive, both reviews would be coded under the 'service' theme. However, one would be marked as having a positive sentiment, and the other would be negative.
This is why it is best to think of thematic coding and sentiment analysis as complementary rather than competing. By combining the two methods, market researchers can not only identify themes in the text but also start to understand the emotion behind them. Using the example above, this restaurant could begin to identify patterns in how service affects the overall customer experience. Combining these two methods together can be an effective way to gain insights into outcomes such as customer churn or NPS.
Displayr's #1 text analytics software does exactly that, coding text with a high level of accuracy and allowing users to utilize prompts to ask highly detailed questions of your data—making text analysis simple.
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