Sentiment Trend Visualization for Local Geographic Events
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Solution Overview
Problem
It is time-consuming and difficult for users to manually read through large volumes of disseminated information in various languages to understand sentiments related to events, especially in local geographic regions, as the information can be scattered across multiple sources and languages.
Innovation Solution
Automated sentiment visualization mechanisms analyze and visualize public commentary from local sources, identifying events and attributes, extracting sentiment words, and generating trend visualizations to depict sentiment trends over time or across attributes, allowing for focused analysis on specific geographic regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually read through large volumes of disseminated information to understand sentiments, then they can gain comprehensive understanding of public commentary, but it is time-consuming and difficult especially when information is in various languages and scattered across multiple sources
Solution Approach 1:
The patent replaces the manual mechanical process of reading and analyzing information with an automated sentiment analysis system that uses natural language processing and machine learning algorithms to extract sentiments from text data across multiple languages and sources, dramatically reducing analysis time while maintaining or improving accuracy
Solution Approach 2:
The patent introduces sentiment analysis algorithms and processing systems as intermediaries between the raw disseminated information and the user, automatically translating, aggregating, and analyzing sentiments from multiple languages and sources to provide comprehensive insights without requiring users to manually process the underlying data
2Productivity
If automated sentiment visualization mechanisms analyze information from multiple sources and languages, then analysis speed and coverage are improved, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional sentiment analysis system that can process multiple languages, analyze various types of text sources (news articles, social media, reviews), and generate different visualization formats simultaneously, reducing the need for separate systems for each function while improving overall productivity
Solution Approach 2:
The patent extracts and isolates specific sentiment-related features and keywords from the complex information streams, focusing analysis on relevant elements while filtering out noise, thereby managing system complexity by concentrating on essential data elements rather than processing all raw information
3Measurement precision
If sentiment analysis focuses on local geographic regions with specific languages, then relevance and applicability of insights are improved, but the volume of information to process increases when covering multiple regional languages
Solution Approach 1:
The patent segments the information processing task by geographic region and language, allowing the system to focus on specific regional sentiment patterns while maintaining the capability to handle multiple regions simultaneously, thereby improving relevance without being overwhelmed by the total volume of global information
Solution Approach 2:
The patent applies language-specific sentiment analysis models and vocabulary databases tailored to each local language and region, improving the precision and cultural relevance of sentiment detection for each specific market while efficiently managing the overall information volume through specialized rather than generic processing
Data Source
AI summary
An event occurring in a particular geographic region is identified based on disseminated information containing public commentary in the particular geographic region. Attributes that are related to the event are identified, and sentiment words relating to the identified event are extracted from the disseminated information, where the extracted sentiment words are in a local language of the particular geographic region. A sentiment trend visualization is generated that depicts a trend of sentiments of at least a particular one of the identified attributes, wherein the sentiments are based on the sentiment words for at least the particular attribute.