Topic Generation from Communication Analysis
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Solution Overview
Problem
Authors face challenges in finding time to brainstorm ideas for publications, leading to infrequent new content, which can result in unsuccessful blogs due to difficulty in remembering their current work focus.
Innovation Solution
A method and system that parse communications through natural language processing to identify keywords, sentiment, and timestamps, determining a publication topic based on keyword frequency, sentiment threshold, and communication timestamps within a specific period, and suggesting content for publication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If authors spend time brainstorming ideas for publications, then content quality and engagement improve, but time available for other work decreases
Solution Approach 1:
The system automatically analyzes the author's own communications (emails, messages, documents) to generate publication topics without requiring external brainstorming assistance. The author's existing work becomes the source material, eliminating the need for separate idea-generation time
Solution Approach 2:
The system continuously monitors and analyzes communications in the background before publication needs arise, pre-identifying potential topics from ongoing work. This preliminary analysis ensures topics are ready when the author needs to publish, eliminating last-minute brainstorming
2Productivity
If authors focus on current work tasks, then work productivity increases, but ability to remember and document work focus for publication decreases
Solution Approach 1:
The system acts as an intermediary between the author's work communications and publication topics. It automatically captures and analyzes communication content, extracting relevant topics without requiring the author to manually remember or document work focus areas
Solution Approach 2:
The system replaces the mechanical process of human memory and manual note-taking with automated natural language processing. The NLP system analyzes communications to identify topics, substituting cognitive effort with computational analysis
3Productivity
If publication content is generated frequently, then follower engagement improves, but content relevance and quality may decrease
Solution Approach 1:
The system applies multiple filtering parameters (keyword frequency thresholds, sentiment analysis scores, recency windows) to evaluate potential topics. Only topics meeting all parameter criteria are selected, ensuring frequent publications maintain high relevance and quality standards
Solution Approach 2:
The system uses sentiment analysis of communication responses as feedback to identify engaging topics. Positive sentiment in communications indicates audience interest, guiding topic selection to ensure published content resonates with followers
Data Source
AI summary
An aspect of topic generation includes parsing communications conducted by users through an application. The communications include a communication generated by a sender and response communications received from recipients in reply to the communication generated by the sender. An aspect also includes identifying keywords, timestamps, and indications of sentiment from the parsed communications through natural language processing, determining a focus of the communication generated by the sender based on the keywords identified from the parsing, and formulating a topic for a publication based on criteria including a frequency of occurrence of the keywords in the parsed communications, a threshold level of the indications of sentiment that appear in the parsed communications, and/or a number of the communications containing one or more of the keywords having corresponding timestamps that fall within a threshold period of time. An aspect further includes submitting the topic for publication to the sender.