Identifying influential segments from word co-occurrence networks using AHP

Abstract

Identifying important segments in textual data seems to be an important area of research for various applications including topic modelling, trend detection, summarization and event detection. In existing research work, different metrics have been studied to analyse the word co-occurrence network. This research work contributes towards non-semantic and an unsupervised topic identification using the word co-occurrence networks. In this research work, keyphrase have been identified by preserving the lexical sequence using a directed and weighted word co-occurrence network. Further AHP (Analytic Hierarchy Process) model based upon four significant attributes of the word co-occurrence networks have been proposed to rank the keyphrases. Most frequently occurring segment is identified as an influential segment. Experimental results proved high effectiveness of the proposed approach. Results for the First Story Detection, 72 Twitter TDT, synthesized Rio Olympics dataset have been discussed to demonstrate its potential in precisely discovering influential segments.