The Conversation Module, powered by AI-based natural language processing and clustering technologies, helps users efficiently navigate massive volumes of text by highlighting high-frequency keywords, trending topics, representative accounts, and content distribution patterns.
This module is ideal for uncovering user focus areas, identifying dissemination structures, and building a semantic tagging system.
1. Conversation Insights
Through semantic parsing and frequency analysis of all collected content, the Conversation Insights section presents the following key information:
Most Mentioned Keywords:
Visualizes a keyword cloud showing the most frequently mentioned core terms within the selected topic (e.g., “cybertruck,” “tesla,” “elonmusk”), helping identify public opinion focus and sentiment trends.
Most Mentioned Hashtags:
High-frequency hashtags (e.g., #cybertruck, #elonmusk) reveal common thematic clusters used by users, aiding in the analysis of community discourse systems and content context.
Most Mentioned Accounts:
A list of frequently mentioned social accounts (e.g., @elonmusk) helps identify key disseminators, originators of conversations, or potential KOLs/KOCs.
Most Used Emojis:
Analysis of the most used emojis in text provides support for detecting emotional tone and tracking user sentiment trends.
Most Referenced Domains:
Identification of frequently linked external websites (e.g., news portals, media outlets, brand homepages) aids in tracking the topic’s dissemination path and source of information.
2. Conversation Distribution
This section provides a multi-dimensional structural analysis to help users understand content composition and dissemination patterns, including:
Media Type Distribution: Categorizes and analyzes content by format such as text, images, videos, links, etc.
Content Type Distribution: Categorized by posts, comments, shares, and more.
Content Publish Time Distribution: Identify posting density patterns and analyze peak activity periods.
Platform-Specific Structural Analysis:
For X (formerly Twitter): Analysis of post types and distribution of client devices.
For Reddit: Analysis of subreddit structures and news source origins.
3. Usage Recommendations
Combine filters (such as platform, language, sentiment) to quickly pinpoint high-frequency mentions within specific regions or user groups.
Use the “drill-down” feature to explore more detailed analyses.
High-frequency keywords and accounts can be directly used to create new listening topics, track dissemination nodes, or trace the origin of public sentiment.
It’s recommended to cross-analyze with the “Sentiment Trend” and “Engagement Trend” modules to assess the sentiment polarity and dissemination intensity of the mentioned keywords.