ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS
type: CreativeWork
description: Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the product and obtain the opinions of each aspect (how positive or negative the appraisal or emotions towards the aspect is). A few systems like Twitter Aspect Classifier and Twitter Summarization Framework have been proposed to perform ABOM on microblogs. However, the accuracy of these techniques are easily affected by spam posts and buzzwords. In this thesis we address this problem of removing noisy aspects in ABOM by proposing an algorithm called Microblog Aspect Miner (MAM). MAM classifies the microblog posts into subjective and objective posts, represents the frequent nouns in the subjective posts as vectors, and then clusters them to obtain relevant aspects of the product. MAM achieves a 50% improvement in accuracy in obtaining relevant aspects of products compared to previous systems.
abstract: Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the product and obtain the opinions of each aspect (how positive or negative the appraisal or emotions towards the aspect is). A few systems like Twitter Aspect Classifier and Twitter Summarization Framework have been proposed to perform ABOM on microblogs. However, the accuracy of these techniques are easily affected by spam posts and buzzwords. In this thesis we address this problem of removing noisy aspects in ABOM by proposing an algorithm called Microblog Aspect Miner (MAM). MAM classifies the microblog posts into subjective and objective posts, represents the frequent nouns in the subjective posts as vectors, and then clusters them to obtain relevant aspects of the product. MAM achieves a 50% improvement in accuracy in obtaining relevant aspects of products compared to previous systems.
author: Ejieh, Chukwuma
language: en_CA
license: http://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2016-01-01
date modified: 2026-07-27T22:04:52Z
provider: University of Windsor
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Windsor

PropertyValue
property ID: Handle
value: https://hdl.handle.net/20.500.14776/6070