Triangulated Sentiment Analysis of Tweets
Loading...
Files
[Triangulated Sentiment Analysis of Tweets]
Authors
Griesser, Simone E
Gupta, Neha
Author (Corporation)
Publication date
14.06.2019
Typ of student thesis
Course of study
Type
06 - Presentation
Editors
Editor (Corporation)
Supervisor
Parent work
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Bern
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Social media platforms like Twitter present an unprecedented opportunity for customer relationship management by analysing the ongoing discussions about business events such as a service outage. These opinions have been analysed for sentiment with lexicon-based and machine learning approaches. Both methods view sentiment as either positive, neutral, or negative. According to the psycholinguistic approach, text sentiment is more continuous reflecting more naturally how we experience emotions. We compare these three approaches with a Twitter dataset collected during a service outage. Contrary to our expectation, we find that the language used in tweets is not very negative or emotionally intense. This research therefore contributes to the sentiment analysis discussion by dissecting three methods and discussing how and why they arrive at differing results. The selected research context provides an illuminating case about service failure and recovery.
Keywords
sentiment, text analysis, emotional intensity, twitter, psycholinguistics
Subject (DDC)
Event
6th Swiss Data Science
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
No
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the abstract
Open access category
License
Citation
Griesser, S. E., & Gupta, N. (2019, June 14). Triangulated Sentiment Analysis of Tweets. 6th Swiss Data Science. https://doi.org/10.26041/fhnw-1752