Real-Time Conversation Data Segmentation for Topic Modeling
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Solution Overview
Problem
Existing text processing techniques for real-time conversation data, such as pre-processing methods like tokenization, stemming, and lemmatization, reduce the number of words available for analysis, making it difficult to infer meaningful topics in medium-sized text corpora like real-time chat discourse, leading to a lack of data for effective topic modeling.
Innovation Solution
Implementing a machine learning mechanism for text segmentation by classifying real-time conversation data into burst segments and reflection segments based on inter-arrival times, which allows for enhanced topic modeling operations and increases the number of words available for analysis, thereby improving the precision and readability of topic models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pre-processing methods like tokenization, stemming, and lemmatization are applied to real-time conversation data, then the data is structured for analysis, but the number of words available for topic modeling is reduced
Solution Approach 1:
The patent segments real-time conversation data into burst segments (rapid successive messages) and reflection segments (messages with inter-arrival times outside the first time interval). This segmentation preserves all original words while creating meaningful units for analysis, avoiding the word reduction that occurs with traditional pre-processing methods.
Solution Approach 2:
The patent performs preliminary classification of messages into burst and reflection segments based on inter-arrival times before topic modeling. This preliminary action organizes the data structure without removing or modifying words, thus maintaining both structural quality and word quantity for subsequent analysis.
2Device complexity
If traditional text processing techniques are used on medium-sized text corpora like real-time chat discourse, then processing is simplified, but meaningful topic inference becomes difficult due to lack of data
Solution Approach 1:
The patent introduces dynamic segmentation that adapts to the temporal patterns in real-time conversation data. By classifying messages based on inter-arrival times and creating burst/reflection segments, the method dynamically adjusts the analysis units to match the conversational flow, improving topic modeling accuracy without requiring complex preprocessing.
Solution Approach 2:
The patent changes the parameter used for data organization from traditional linguistic features (used in tokenization, stemming, lemmatization) to temporal parameters (inter-arrival times between messages). This parameter change enables meaningful topic inference in medium-sized corpora by preserving more words while creating structurally meaningful units for analysis.
Data Source
AI summary
Embodiments for text segmentation for topic modelling by a processor. A machine learning mechanism is implemented to enhance one or more topic modelling operations of collected real-time conversation data, the real-time conversation data classified into burst segments or reflection segments; wherein the burst segments comprise successive messages received into the conversation data within a first time interval and the reflection segments comprise multiple messages each received into the conversation data having an inter-arrival time outside the first time interval. A summary of the one or more topic modelling operations is presented on a display to a user according to an output of a text mining analysis implementing the one or more topic modelling operations enhanced by the machine learning mechanism.