Data Analysis and Insight Generation with Queryable Knowledge Graphs

In the dynamic and competitive landscape of the technology market, understanding customer sentiments is paramount for success. This research explores the integration of knowledge graphs, sentiment analysis, and social media data to extract user insights and identify trends within the technology market. Leveraging Twitter data, the study employs web scraping techniques and custom sentiment analysis models to analyze product-related discussions. Key phrase generation is utilized to extract meaningful information, which is then structured into a knowledge graph. The resulting graph enables enhanced visualization and understanding of user sentiments and product preferences using the concept of community detection.