Description
Title: NEW FRAMEWORK FOR ASPECT KNOWLEDGE BASE GENERATED AUTOMATICALLY FROM SOCIAL MEDIA USING PATTERN RULES
Abstract: Systems for creating summaries based on social media sentiment are one of the elements that help businesses with business intelligence. These systems use annotated datasets, but they are unable to automatically generate such summaries. We suggest a novel framework that makes use of pattern rules to support these systems with annotated datasets. There are two steps in the framework: 1) Preprocessing; and 2) Creation of an aspect knowledge base. The first step is to check for any misspelled words (bigrams and unigrams), correct them using a suggested method, and tag the parts of speech for each word. The second step involves automatically creating an aspect knowledge base that will be used by sentiment-summarization systems to create sentiment summaries. An aspect knowledge base from social media is automatically generated using pattern rules and semantic similarity-based pruning. Eight domains from benchmark datasets of reviews are used in the experiments. When compared to other unsupervised approaches, the performance evaluation of our suggested approach demonstrates the highest performance.
Keywords: opinion mining, aspect knowledge base, aspect extraction, pattern rules, social media
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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