Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream

TL;DR

This paper presents a new preprocessing algorithm for classifying Twitter data streams into specific, mutually exclusive categories to improve the efficiency and relevance of data mining applications.

Contribution

The paper introduces a subject-specific stream classification algorithm that enhances data mining efficiency on Twitter by accurately categorizing data streams.

Findings

Improved classification accuracy for Twitter data streams

Enhanced efficiency of data mining processes

Potential for more relevant sentiment analysis results

Abstract

Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item; it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams upon each of which the data mining algorithm can be run separately yielding more relevant results with a high efficiency.