Login | Register

ENHANCING SENTIMENT LEXICA WITH NEGATION AND MODALITY FOR SENTIMENT ANALYSIS OF TWEETS

Title:

ENHANCING SENTIMENT LEXICA WITH NEGATION AND MODALITY FOR SENTIMENT ANALYSIS OF TWEETS

ÖZDEMİR, CANBERK BERKİN (2015) ENHANCING SENTIMENT LEXICA WITH NEGATION AND MODALITY FOR SENTIMENT ANALYSIS OF TWEETS. Masters thesis, Concordia University.

[img]
Preview
Text (application/pdf)
Ozdemir_MCompSc_F2015.pdf - Accepted Version
Available under License Spectrum Terms of Access.
506kB

Abstract

Sentiment analysis became one of the core tasks in the field of Natural Language Processing especially with the rise of social media. Public opinion is important for many domains such as commerce, politics, sociology, psychology, or finance. As an important player in social media, Twitter is the
most frequently used microblogging platform for public opinion on any topic.
In recent years, sentiment analysis in Twitter turned into a recognized shared task challenge. In this thesis, we propose to enhance sentiment lexica with the linguistic notions negation and modality for this challenge. We test the interoperability between various sentiment lexica with each other and with negation and modality and add some Twitter-specific ad-hoc features. The performance of different combinations of these features is analyzed in comprehensive ablation experiments.
We participated in two challenges of the International Workshop on Semantic Evaluations (SemEval 2015). Our system performed robustly and reliably in the sentiment classification of tweets task, where it ranked 9th among 40 participants. However, it proved to be the state-of-the-art for measuring degree of sentiment of tweets with figurative language, where it ranked 1st among 35 systems.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:ÖZDEMİR, CANBERK BERKİN
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science and Software Engineering
Date:27 August 2015
Thesis Supervisor(s):Bergler, Sabine
ID Code:980377
Deposited By: CANBERK BERKIN OZDEMIR
Deposited On:03 Nov 2015 17:09
Last Modified:05 Nov 2016 07:48
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top