AI-Powered solution to uncover hidden trends
in millions of articles
BACKGROUND
- Opentopic (now defunct) faced a challenge of extracting valuable insights from a vast ocean of online articles.
- Manually processing such data was impossible and gathering insights requires using domain experts.
- Opentopic needed a system to automatically gather, classify, and analyze massive datasets for trend identification.
IMPACT
- Save significant time and resources by automating data collection and analysis.
- Gain deeper understanding of public sentiment and emerging trends.
OBJECTIVES
Develop an AI-powered solution for Opentopic that could automate the entire data processing pipeline. This included collecting articles from the web, classifying them by category, analyzing sentiment, extracting
predicting author age. Ultimately, the goal was to transform massive datasets into actionable insights through trend identification and concise data summaries.
SOLUTIONS
Solution tackled Opentopic’s data challenge head-on. We leveraged web scraping techniques to gather online articles and employed machine learning to categorize them by a pre-defined system. Sentiment analysis models determined the overall tone of each article, while advanced algorithms identified key entities and analyzed the sentiment surrounding them. Furthermore, the system extracted author information, including predicted age based on writing style, and captured the main image and publication date of each article. Finally, BlueRider.Software designed algorithms to identify trends within the data, focusing on shifts in sentiment or topic mentions over time. By creating summaries that highlighted key points, the solution empowered Opentopic to gain valuable insights from vast amounts of data with remarkable efficiency.
Data Scraping
Leveraged web scraping techniques to gather relevant articles from diverse online sources.
Machine Learning for Classification
Employed machine learning algorithms to categorize
articles based on a pre-defined taxonomy.
Sentiment Analysis Models
Implemented sentiment analysis models to determine the
overall tone of each article.
Entity Recognition and Sentiment Analysis
Extracted key entities and analyzed the sentiment
expressed around them.
Author & Content Extraction
Developed algorithms to identify authors, predict their age
based on writing patterns, and extract clean content from
HTML pages.
Image & Publication Date
Extraction
Utilized techniques to capture the main image and
publication date associated with each article.
Trend Recognition Algorithms
Designed algorithms to identify trends in specific
configurations of parameters within the data, like shifts in
sentiment towards particular entities over time.
Data Summarization Logic
Built a system to generate summaries that highlight key
points and insights from the analyzed data, providing
users with a concise overview.
2.8 minutes
Read 700-word article
0.1 minutes
Read 700-word article
3 minutes
Classify article by
category
0.1 minutes
Classify article by
category
1.5 minutes
Analyze sentiment of
article
0.1 minutes
Analyze sentiment of
article
4.5 minutes
Extract entities from
article
0.1 minutes
Extract entities from
article
Is it possible?
Analyze dependencies
across all parameters
from 1 million articles
Hours
Analyze dependencies
across all parameters
from 1 million articles
