Title:	Reliable General Purpose Sentiment Analysis of the Public Twitter Stream
Authors:	Haldenwang, Nils
Thesis advisor:	Prof. Dr. Oliver Vornberger
Thesis referee:	Prof. Dr. Frank Köster
Abstract:	General purpose Twitter sentiment analysis is a novel field that is closely related to traditional Twitter sentiment analysis but slightly differs in some key aspects. The main difference lies in the fact that the novel approach considers the unfiltered public Twitter stream while most of the previous approaches often applied various filtering steps which are not feasible for many applications. Another goal is to yield more reliable results by only classifying a tweet as positive or negative if it distinctly consists of the respective sentiment and mark the remaining messages as uncertain. Traditional approaches are often not that strict. Within the course of this thesis it could be verified that the novel approach differs significantly from the traditional approach. Moreover, the experimental results indicated that the archetypical approaches could be transferred to the new domain but the related domain data is consistently sub par when compared to high quality in-domain data. Finally, the viability of the best classification algorithm could be qualitatively verified in a real-world setting that was also developed within the course of this thesis.
URL:	https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017092716282
Subject Keywords:	Sentiment Analysis; Text Analysis; Data Mining; Twitter; Social Media Analysis; Twitter Sentiment Analysis; General Purpose Twitter Sentiment Analysis; Machine Learning
Issue Date:	27-Sep-2017
License name:	Namensnennung-NichtKommerziell-KeineBearbeitung 3.0 Unported
License url:	http://creativecommons.org/licenses/by-nc-nd/3.0/
Type of publication:	Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:	FB06 - E-Dissertationen

This item is licensed under a Creative Commons License