Developments in Artificial Intelligence have made natural language processing more accurate and valuable in the interpretation of qualitative big data. This big data is made more accessible to businesses in 3 ways.
How Natural Language Processing Analyses Qualitative Data
I come from an anthropological background, where qualitative data is king. Ethnographies, the research method of the anthropology discipline, emphasises qualitative research to understand the complexity of the society of study. Qualitative data, expressed verbally or through user comments, provide valuable insight on communities that cannot be quantified through surveys or ratings. Drawing a parallel to business, I see a similar focus. Businesses are increasingly using on-the-ground big data to better understand and address the needs of their consumers and clients.
This may be due to the slow realisation of the shortcomings of quantitative data. However, this can also be attributed to how big data analytics is making qualitative data more accessible to businesses. In the past, qualitative data does not lend itself to the clear cut analysis of quantitative data. However with advancements in Artificial Intelligence, natural language processing, and sentiment analysis can interpret qualitative values more easily and with more accuracy than before. Although it must be said that there is still much development to be done.
Interestingly, applications of natural language processing have extended to the social sciences, as detailed by Crowston et al.’s journal article linked here. Perhaps this speaks to the growing accuracy of natural language processing. The use of high-level natural language processing techniques are used to analyse qualitative data analysis partially- a development most social scientists and researchers would never have bet on as the human element of interpreting data has never been replaceable.
Benefits of Qualitative Data
The value of qualitative data for businesses comes directly from the consumer. The data is highly specific, relevant and without filter or generalisation. People are complex in their decision making and this complexity of thought is not reflected in quantitative ratings. Crowdsourcing information is beneficial as it allows businesses to hear directly from stakeholders without a filter.
Qualitative data allows businesses to understand consumer behaviour better and why certain consumers respond to products in a particular manner. It allows businesses to build accurate customer profiles to inform communication and marketing efforts.
The 3 ways accessing qualitative data has become easier than ever for businesses:
1. Big data analytics:
Big data analytics make understanding the large amounts of qualitative data more comprehensible. Qualitative research has no bounds, and while the breadth and depth of information it provides may be useful, it is also a nightmare for businesses to analyse and understand. Some data points may be outliers and not a true reflection of the overall consumer market. This is where big data analytics is useful in aggregating and organising chatter and packaging this information neatly.
2. Social Listening:
Software, like the Latent App, can analyse user comments at scale in real time. Artificial Intelligence can now compute the human element of language better than ever. The nuances of comments can be accurately analysed, for example Latent App is able to understand Singapore colloquial slang. This provides more accurate big data analytics for businesses. Using intelligence from natural language processing, outbound conversion efforts and solutions can be made more effective and tailored to consumers.
3. Sentiment Analysis:
Artificial Intelligence advancements allow businesses to monitor the trajectory of sentiment generated. This allows you to observe trends in sentiment. For example, businesses can evaluate the efficacy of specific marketing campaigns in generating interest or even monitor a crisis. Sentiment analysis allows businesses to address and improve customer satisfaction immediately.
Qualitative data is harder to collect but is invaluable in making informed business decisions. Now with the advance of Artificial Intelligence, it is becoming easier and easier for businesses to access. Latent App provides exactly this for businesses. We are a big data SaaS that analyses opinionated user comments at scale. Focus on your user value add, while we provide the data and models.