title   
  

SARE: A sentiment analysis research environment

Husaini, Mus'ab Habib (2013) SARE: A sentiment analysis research environment. [Thesis]

[img]PDF - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1501Kb

Official URL: http://risc01.sabanciuniv.edu/record=b1534388 (Table of Contents)

Abstract

Sentiment analysis is an important learning problem with a broad scope of applications. The meteoric rise of online social media and the increasing significance of public opinion expressed therein have opened doors to many challenges as well as opportunities for this research. The challenges have been articulated in the literature through a growing list of sentiment analysis problems and tasks, while the opportunities are constantly being availed with the introduction of new algorithms and techniques for solving them. However, these approaches often remain out of the direct reach of other researchers, who have to either rely on benchmark datasets, which are not always available, or be inventive with their comparisons. This thesis presents Sentiment Analysis Research Environment (SARE), an extendable and publicly-accessible system designed with the goal of integrating baseline and state of- the-art approaches to solving sentiment analysis problems. Since covering the entire breadth of the field is beyond the scope of this work, the usefulness of this environment is demonstrated by integrating solutions for certain facets of the aspect-based sentiment analysis problem. Currently, the system provides a semi-automatic method to support building gold-standard lexica, an automatic baseline method for extracting aspect expressions, and a pre-existing baseline sentiment analysis engine. Users are assisted in creating gold-standard lexica by applying our proposed set cover approximation algorithm, which finds a significantly reduced set of documents needed to create a lexicon. We also suggest a baseline semi-supervised aspect expression extraction algorithm based on a Support Vector Machine (SVM) classifier to automatically extract aspect expressions.

Item Type:Thesis
Uncontrolled Keywords:Sentiment analysis. -- Opinion mining. -- Aspect lexicon extraction. -- Set cover approximation. -- Integrated research environment. -- Duygu analizi. -- Düşünce madenciliği. -- Görüş sözlüğü çıkarımı. -- Set kaplama yaklaştırımı. -- Entegre araştırma ortamı.
Subjects:Q Science > QA Mathematics > QA076 Computer software
ID Code:31183
Deposited By:IC-Cataloging
Deposited On:07 Apr 2017 14:32
Last Modified:07 Apr 2017 14:32

Repository Staff Only: item control page