Basing product decisions on how consumers feel about a good or service sounds like a no-brainer, but the reality of conducting sentiment analysis and uncovering usable insights is much harder than it seems. When making new product decisions or considering improvements to existing products, consumer goods researchers often struggle with the sheer complexity of human emotions, extensive channels for qualitative data, and language and cultural barriers.
While this might seem like an insurmountable ask of your market research team, it’s work worth doing as it has the potential to skyrocket consumers’ perception of your product and increase company profits.
In this article, we’ll cover some of the common challenges consumer researchers face when performing sentiment analysis, tips to overcome them, and ways to increase efficiency and deep learning that can improve your product and increase profits.
Common Sentiment Analysis Challenges:
- Complexity of Human Emotions: Not only do people have multifaceted emotions that can exist simultaneously, but many people also struggle to convey how they feel in succinct words or phrases. Unpacking the true meaning of someone’s statement and properly grouping the data through sentiment classification takes dedicated time and attention from researchers, especially when working with unstructured data.
- Extensive Qualitative Data Channels: While traditional qualitative data collection methods such as surveys and forums are alive and well, new sources of data have come forth with the rise in technology. People now communicate their positive or negative sentiments, thoughts, opinions, and feelings on products online through social media, reviews, and community forums. The ever-increasing volume of video, audio, and text data creates both new opportunities and more opinion mining and sentiment analysis work to properly gauge overall sentiment.With each channel, consumers express themselves differently – not just in terms of long- or –short-form content, but also in the level of honesty of their answers. This could depend on whether their friends and family can see their responses (e.g., customer feedback on social media platforms) or whether their identity remains anonymous.
“The benefits of using an anonymous market research survey for SaaS businesses include increased accuracy of data, increased privacy for customers, improved customer experience, increased trust in the organization collecting the data, and improved data collection efficiency.” – Vimala Balamurugan, BlockSurvey.
- Language and Cultural Barriers: Translating qualitative data is fraught with challenges – from translation errors to simply not having the same words to express the exact sentiment in a different language. Additionally, words themselves mean different things to people depending on where they are from, regardless of if the words were translated. It’s crucial for consumer goods researchers to keep these differences in mind, and it’s often recommended to work with someone who knows the original language or culture when interpreting sentiment expressed.
Efficiently Analyze Large Datasets for Sentiment Analysis with NVivo
While AI still struggles to decipher some human expressions, such as sarcasm, intelligent qualitative data analysis software like NVivo can help you sort through lengthy rows of data for customer sentiment – saving you time and letting you focus on uncovering the true meaning of your data.
Compiling Video, Audio, and Textual Data from Multiple Sources
Collecting and compiling your qualitative data is the first step to analyzing sentiment analysis. Traditionally, this process can be tedious when pulling from multiple sources that format their data in different ways, but some of the most valuable feedback from consumers happens in places where companies aren’t actively asking for feedback (e.g., Instagram, X, Facebook, Google online reviews, and Yelp). With NVivo, you can easily import text, audio, images, online surveys, social media comments, videos, and more all into one user-friendly interface to facilitate accurate sentiment analysis.
If working with interview, audio, or video data, you can leverage AI in NVivo Transcription to automatically transcribe audio and video files using natural language processing (nlp) with 90% accuracy – speeding up the time between data collection and sentiment analysis. Learn more about how NVivo Transcription can work as your automated transcription assistant and help you focus on your sentiment analysis in our on-demand webinar NVivo Transcription – Going Beyond Words.
AI-Powered Autocoding for Sentiment Analysis
With your dataset in one place, the sentiment analysis process can begin. Instead of sorting through each row scanning for commonalities, NVivo’s AI-powered autocoding efficiently groups data for themes and sentiment.
It does this by conducting text analysis – locating emotive words and assigning a sentiment score. This score ranges from minus one to positive one and considers the accompaniment of a modifier (e.g., more, very). Once the autocoding is complete, NVivo generates a chart of the codes and number of coding references to help you visually digest your data – clearly showing positive and negative sentiments. By letting machine learning take the first round of coding and using the sentiment analysis model, you not only speed up the process but also open yourself up to seeing patterns and groupings that might have been missed.
NVivo’s centralization of data and autocoding for sentiment analysis allows you to compile and analyze more data – resulting in a deeper understanding of how your consumers feel about a product or service and helping you draw confident conclusions that inform your next step.
Use Sentiment Analysis Tool NVivo with a Free Trial
Learn more about NVivo’s machine learning sentiment analysis solution in our short video AI-Powered Autocoding by Sentiment, and try it out for yourself with a 14-day free trial of NVivo.