AI-RMS: Artificial Intelligence Based Reputation Management System

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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

  1. 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

  1. 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.
  1. Machine Learning Phase
    • Aggregates outputs from the feature extraction phase to compute final reputation scores.
    • Generates trends to visualize how reputation evolves over time.
  2. 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

  1. 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.
  2. 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.

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