Overview
AI-RMS is an advanced Artificial Intelligence-based Reputation Management System designed to automate the analysis and monitoring of online reputation in social media. It addresses challenges faced by domain experts by integrating state-of-the-art technologies to analyze reputation trends and factors in real time. The system calculates reputation scores by aggregating predictions from three critical factors: Source, Passion, and Scope. It also provides tools for reputation forecasting and investigation, enabling entities to manage their reputations proactively.
Fig: AI-RMS System Architecture
System Architecture and Workflow
- Real-Time Monitoring Phase
- Input: Continuous streams of social media text data and user-specific attributes.
- Processing: Filters and processes text streams to extract entity-relevant data.
- Output: Structured textual and user metrics related to the target entity.
Fig: BERT Architecture for Reputation Polarity Task
- Feature Extraction Phase
- Source Factor Analysis:
- Social network graphs are constructed using NetworkX, leveraging node and edge properties to calculate influence metrics.
- Regression models quantify the Source Influence Score.
- Passion Factor Analysis:
- Text classification is performed using a fine-tuned BERT model, analyzing sentiment to compute the Text Reputation Polarity.
- Scope Factor Analysis:
- A BERT-based classifier assigns a Reputation Priority Class to textual data based on its relevance to reputation.
- Source Factor Analysis:
- Machine Learning Phase
- Aggregates outputs from the feature extraction phase to compute final reputation scores.
- Generates trends to visualize how reputation evolves over time.
- Reputation Forecasting and Investigation Phase
- Employs ARIMA (Auto Regressive Integrated Moving Average) for predicting future reputation trends.
- Tools include:
- Topic Investigation Tool: Identifies the most impactful topics using Latent Dirichlet Allocation (LDA) and domain-specific dictionaries.
- Audience Research Tool: Highlights key influencers and their influence scores from the social network.
Fig: Reputation Trends of Bank of America for the year 2012
Key Features
- Real-Time Monitoring: Automatically extracts and updates reputation metrics from social media in real time.
- Feature Extraction with NLP and Social Networks: Combines textual sentiment analysis with social network features for a comprehensive understanding of reputation dynamics.
- Forecasting and Management Tools:
- Predicts future reputation trends.
- Provides actionable insights through topic and audience analysis.
- Interactive Visualizations: Graphical representations of reputation trends, source networks, and influential topics.
Tools and Technologies
- Natural Language Processing: BERT (Bidirectional Encoder Representations from Transformers) for reputation polarity and priority analysis.
- Social Network Analysis: NetworkX, K-shell decomposition, Core Centrality, Eigenvector Centrality
- Regressors & Classifiers: Random Forest, XGboost, LGBoost, Lasso, Ridge.
- Time-Series Modeling: Auto Regressive Integrated Moving Average (ARIMA)
- Clustering and Topic Analysis: Latent Dirichlet Allocation (LDA), Gaussian Mixture Model (GMM), Spectral Clustering, DBSCAN
- Programming and Platforms: Python
- Data Collection: Twitter API, Tweepy
Outcomes and Impact
- Performance Improvements:
- Enhanced reputation polarity detection accuracy by 5.8%.
- Improved balanced accuracy by 26.9%.
- Achieved a 21.8% increase in F1-score for reputation scoring tasks.
- Business Benefits:
- Offers actionable insights for proactive reputation management.
- Empowers companies and public figures to address reputational risks effectively.
AI-RMS showcases a pioneering integration of machine learning, natural language processing, and social network analysis, offering a robust solution for reputation management in the digital age. Its real-time monitoring, predictive capabilities, and action-oriented tools make it a transformative system for public relations and brand management.