A comparison of text network analysis, dictionary-based AFINN, and machine-learning based BERT AI models for sentiment analysis of customer reviews.
How to reveal the main topics in the positive and in the negative customer reviews in order to see what makes them happy and what makes them dislike the product.
How to improve sentiment analysis using text network visualization that helps you see not only the most prominent topics but also the correlations between the responses of customers. Using the example of online surveys and Twitter data.
How to build brand association maps using customer feedback and visualize the main topics as well as the connections between them.
Any group of people working together has a field of interests, which can be represented as a network of interrelated concepts. Mark might be interested in studying trends and fashion, while Joanne might be into fashion and arts. Identifying those interests and their relation to one another can be very useful for improving the collaboration, indicating the vantage points as well as the structural gaps within the group.
Text network analysis can be used for such quick group profiling. The members of the group can write a short text (or be interviewed) outlining their interests. Alternatively, publicly available texts can be used to gather the data. Next, graph visualization of this aggregated text can help identify the most prominent topics and their relations within that text corpus.
