“Automated Brand Reputation Management System with Real-Time Sentiment Monitoring, Crisis Detection, and Corrective Action Recommendations”

Abstract

This patent describes an Automated Brand Reputation Management System designed to monitor and manage brand sentiment in real-time across social media, review sites, and news outlets. The system utilizes advanced natural language processing (NLP) and machine learning (ML) to analyze sentiment and detect emotional nuances, such as sarcasm and regional language expressions, providing a comprehensive view of brand reputation. By implementing sentiment trend analysis and predictive time-series models, the system offers early alerts on potential reputation risks, enabling proactive crisis management. Unique features include contextual tagging to categorize mentions by issue type (e.g., product quality, corporate ethics), automated corrective action recommendations, and competitor benchmarking. The system also calculates a proprietary reputation score based on sentiment, engagement, and influencer impact, giving brands a measurable indicator of their reputation health. With multi-language and regional adaptability, this system is ideal for global brands looking to safeguard their reputation. It offers a powerful solution for continuous reputation management, early crisis detection, and informed response strategies, enhancing brand trust and consumer engagement.

Specification

Description:Field of the Invention:

This invention relates to the field of brand management and digital reputation monitoring. Specifically, it pertains to an automated system that utilizes natural language processing (NLP) and machine learning (ML) to monitor brand sentiment across multiple platforms in real-time, detect emerging reputation risks, and provide actionable recommendations. The system is designed for scalability, multi-language support, and regional adaptability, enabling brands to understand sentiment trends, benchmark against competitors, and proactively manage their digital reputation across diverse digital channels, including social media, review platforms, and news outlets.

Background of the invention and related prior Art:
Brand reputation management has traditionally been a reactive process, relying on manual monitoring of customer feedback across various digital platforms. As digital interactions have increased, so has the complexity and volume of data generated across social media, review sites, and news outlets. Businesses today must not only track customer opinions in real-time but also respond promptly to prevent negative events from escalating into full-blown reputation crises. However, traditional solutions are often limited in their ability to monitor sentiment effectively, particularly in detecting nuances like sarcasm, regional language variants, and specific emotional tones (e.g., anger, joy, or frustration). Existing systems typically rely on simple keyword-based sentiment analysis, which may fail to capture contextual sentiment accurately. These systems lack the ability to tag crises contextually by categories like product quality or customer service, resulting in generic, often delayed responses to emerging issues. Moreover, many of these solutions are limited to single-language processing, making it challenging for global brands to monitor sentiment effectively across regions. The absence of predictive capabilities also limits their usefulness in detecting early signs of reputation risks. While certain prior art systems offer competitive benchmarking, they often lack comprehensive sentiment tracking and do not prioritize issues based on the impact or suggest tailored corrective actions. This invention aims to address these shortcomings by providing a fully integrated, automated solution that combines advanced NLP with real-time data collection, context tagging, and a recommendation engine, enhancing the ability of brands to manage and protect their digital reputation.

Summary of the invention:
The invention, an Automated Brand Reputation Management System, is designed to empower businesses with tools to monitor and manage their brand reputation in real-time. The system leverages NLP and ML to provide deep insights into brand sentiment across various platforms, including social media, review sites, and news outlets. It continuously tracks sentiment trends and detects nuanced expressions such as sarcasm and specific emotional responses, offering detailed sentiment analysis across multiple languages and regional dialects. A unique feature of the system is its ability to tag detected issues by context (e.g., product issues, customer service, or corporate ethics), enabling brands to respond with contextually relevant actions. Additionally, the system provides predictive capabilities through sentiment trend analysis and time-series forecasting, alerting brands to potential crises before they escalate. The system further distinguishes itself by suggesting corrective actions based on detected issues, historical data, and response effectiveness, allowing brands to address specific concerns and enhance customer trust proactively. It also includes competitor benchmarking, enabling a comparison of brand sentiment and reputation scores with industry peers. By incorporating a proprietary reputation score, the system provides an at-a-glance measure of overall brand health.

Detailed description of the invention with accompanying drawings:
System Architecture
The Automated Brand Reputation Management System consists of interconnected modules designed to monitor brand sentiment, detect potential crises, and recommend corrective actions in real time. The architecture is composed of several key components: the Data Collection Module, Data Ingestion Pipeline, NLP Sentiment Analysis Module, Context Detection Module, Recommendation Engine, Competitor Benchmarking Module, and Reputation Score Calculation Module.
The following sections describe each component in sequence:
1. Data Collection Module
The Data Collection Module is responsible for gathering brand-related data from various platforms, including social media sites, review platforms, forums, and news sources. This module integrates with APIs provided by these platforms to continuously capture brand mentions and associated metadata, such as timestamp, location, and engagement metrics. It supports diverse data sources and types (e.g., text, images) to provide comprehensive brand monitoring. Key tools and technologies:
a) API Integration: Connects with social media and review platforms (e.g., Twitter, Facebook, Google Reviews) to fetch brand mentions.
b) News Aggregation: Uses web scraping or APIs from news platforms to collect relevant news articles.
c) Data Processing Pipeline: Uses tools like Apache Kafka or AWS Kinesis to handle the incoming data in real-time and stream it for further analysis.
2. Data Ingestion Pipeline
The Data Ingestion Pipeline is responsible for ingesting, pre-processing, and storing collected data for efficient processing. This pipeline processes and structures raw data into a format suitable for analysis by subsequent modules. Key features include:
a) Streaming Processing: Apache Kafka or AWS Kinesis enables continuous ingestion, supporting high volumes of real-time data.
b) Data Storage: The system stores structured data in databases like MongoDB (for NoSQL storage) or Elasticsearch (for efficient indexing and querying) for quick access by the analysis modules.
c) Data Cleansing and Transformation: The pipeline includes routines to handle missing data, remove duplicates, and standardize text.
3. NLP Sentiment Analysis Module
This module performs sentiment analysis on the collected data using advanced Natural Language Processing (NLP) techniques. It utilizes transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) or RoBERTa, which are fine-tuned specifically for sentiment detection and can accurately handle nuanced language, including sarcasm and emotion.
a) Sentiment Detection: The model classifies mentions as positive, negative, or neutral, as well as identifies specific emotions (e.g., anger, joy, surprise) to gain deeper insight.
b) Sarcasm and Emotion Tracking: Additional NLP layers enable the detection of sarcastic comments, which are challenging for basic sentiment classifiers.
c) Output: Provides a sentiment score and an emotion classification for each mention.
4. Context Detection Module
The Context Detection Module is responsible for identifying the context of each mention, categorizing it based on specific issues such as product quality, customer service, or corporate responsibility. It uses context-aware embeddings and fine-tuning with domain-specific data to improve context categorization.
a) Issue Categorization: The model categorizes mentions into predefined categories that indicate specific issues affecting the brand.
b) Context-Aware Embeddings: Using embeddings derived from fine-tuned models, this module understands the specific context of each brand mention.
c) Domain-Specific Fine-Tuning: The module is trained on brand-specific data, allowing it to classify context with high accuracy.
5. Recommendation Engine
The Recommendation Engine suggests corrective actions based on detected issues, historical data, and response effectiveness. Recommendations are tailored to the type and severity of the detected issue.
a) Issue-Based Recommendations: The engine selects responses specific to detected issues, such as issuing a public apology, providing additional customer support, or enhancing product information.
b) Severity-Level Prioritization: Based on a predefined severity scale, the engine prioritizes responses for issues that may lead to a crisis.
c) Historical Data Utilization: By analyzing previous responses, the system optimizes recommendations for maximum effectiveness.
6. Competitor Benchmarking Module
The Competitor Benchmarking Module tracks competitor brands' sentiment trends and compares them to the target brand's sentiment metrics. This module helps to position the brand in its market and provides insights into competitor actions that positively or negatively impact their sentiment.
a) Sentiment Tracking of Competitors: The system continuously monitors competitors' sentiment data to benchmark against the target brand's sentiment.
b) Market Position Analysis: This module enables brands to understand where they stand in relation to competitors based on consumer sentiment, providing a comparative reputation score.
7. Reputation Score Calculation Module
The Reputation Score Calculation Module synthesizes data from the sentiment analysis, context detection, competitor benchmarking, and recommendation effectiveness to compute an overall brand reputation score. This score is a weighted calculation based on sentiment, engagement metrics, influencer impact, and historical reputation data.
a) Weighted Sentiment Calculation: Scores are derived from weighted averages of sentiment polarity, volume of mentions, and engagement metrics.
b) Formulas for Scoring Logic:
c) Reputation Score = (α * Sentiment) + (β * Engagement) + (γ * Competitor Impact)
where α, β, and γ are weights assigned based on brand priorities.
d) Influencer and Engagement Metrics: Incorporates engagement level from high-influence accounts to adjust the overall reputation score, considering the impact of influencers on public sentiment.
Advantages of the invention:
The Automated Brand Reputation Management System offers several key advantages. By providing real-time sentiment monitoring across diverse digital channels, brands can respond to reputation risks faster than with traditional systems. Advanced NLP features allow the system to understand complex expressions, including sarcasm and emotional tones, across multiple languages, making it highly adaptable for global brands.
Predictive trend analysis helps brands anticipate crises before they develop, giving them a critical window for proactive response. Contextual tagging of issues enables brands to categorize and address specific areas of concern effectively, from product complaints to corporate ethics issues. Automated corrective action recommendations streamline response strategies, improving the efficiency and accuracy of reputation management efforts. Competitor benchmarking and a proprietary reputation score offer an edge by allowing brands to evaluate their standing relative to competitors, making this system a powerful tool in maintaining brand reputation.
, Claims:We Claim
1. A real-time monitoring system for detecting brand sentiment across social media, review platforms, and news outlets.
2. An NLP-based sentiment analysis engine capable of detecting emotional tones, sarcasm, and regional language sentiment.
3. A context detection module that categorizes mentions into predefined contexts like product quality, customer service, or corporate ethics.
4. A sentiment trend analysis module that uses time-series analysis models to predict potential sentiment shifts.
5. An automated recommendation engine for suggesting corrective actions based on detected sentiment issues.
6. A competitor benchmarking feature to monitor and compare sentiment with industry peers.
7. A multi-language and region-specific sentiment analysis system.
8. A proprietary reputation score calculation system that combines weighted sentiment, influencer impact, and customer engagement.
9. A dashboard interface for real-time sentiment monitoring and trend visualization.
10. An alerting system for notifying brand managers of potential reputation crises based on sentiment forecasts.

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