Probabilistic Message Categorization via Stemming and Frequency Analysis
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Solution Overview
Problem
Current methods for categorizing social media messages are inefficient in determining relevance and intent, particularly in identifying purchase intentions from vast amounts of data, due to the lack of effective filtering and analysis techniques.
Innovation Solution
A system utilizing a probabilistic engine that processes messages by removing stop words, replacing stem words, and determining frequency to calculate relevancy scores, which categorizes messages into relevant categories based on training data, including lists of previously categorized messages, and identifies intent by analyzing word frequencies and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional message categorization methods are used, then processing speed is maintained, but accuracy in identifying relevant messages and purchase intentions deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing messages through stop word removal and stemming before categorization. Training data is pre-collected and labeled with relevant categories and purchase intent indicators, enabling the probabilistic model to be trained in advance. This preliminary preparation improves processing accuracy without significantly impacting real-time processing speed during actual message categorization.
2Reliability
If comprehensive analysis of all messages is performed, then identification of purchase intentions improves, but processing time increases
Solution Approach 1:
The system extracts only the most relevant features from messages for categorization and purchase intent detection. Specifically, it removes stop words that do not contribute to meaning, applies stemming to reduce words to their root forms, and focuses analysis on key terms and phrases that indicate purchase intent. This selective extraction maintains high reliability in identifying purchase intentions while significantly reducing processing time by eliminating unnecessary analysis of irrelevant message components.
3Measurement precision
If training data is continuously updated with user inputs, then categorization accuracy improves over time, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where user inputs and corrections are continuously incorporated into the training data. User feedback on categorization accuracy and purchase intent identification is used to retrain and refine the probabilistic models. This feedback loop progressively improves categorization accuracy over time while the system complexity is managed through automated retraining processes and structured data management protocols.
Data Source
AI summary
Systems and methods of the present disclosure are directed to categorizing messages. A first server can receive, from a second server maintaining a plurality of social media messages, a message. The first server can categorize the message under a first category or a second category. The first server can process the message and determine a frequency of each of the words included in the processed message. The first server determines, using a probabilistic engine executing on the first server, a relevancy score of the modified message indicating a level of relevance between the message and the first category based on the determined frequency of each of the words. The first server, responsive to determining that the relevancy score satisfies a threshold, can categorize the message under the first category.