Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Clustering based personality prediction on turkish tweets
Date
2019-08-30
Author
Tutaysalgir, Esen
Karagöz, Pınar
Toroslu, İsmail Hakkı
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
265
views
0
downloads
Cite This
In this paper, we present a framework for predicting the personality traits by analyzing tweets written in Turkish. The prediction model is constructed with a clustering based approach. Since the model is based on linguistic features, it is language specific. The prediction model uses features applicable to Turkish language and related to writing style of Turkish Twitter users. Our approach uses anonymous BIGS questionnaire scores of volunteer participants as the ground truth in order to generate personality model from Twitter posts. Experiment results show that constructed model can predict personality traits of Turkish Twitter users with relatively small errors.
Subject Keywords
Personality analysis
,
Twitter
,
Clustering
,
Text mining
URI
https://hdl.handle.net/11511/37038
DOI
https://doi.org/10.1145/3341161.3343513
Conference Name
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Clustering based personality prediction on Turkish Tweets
Tutaysalgır, Esen; Toroslu, İsmail Hakkı; Department of Computer Engineering (2019)
In this thesis, we present a framework for predicting the personality traits of users using their tweets written in Turkish. The prediction model is constructed with a clustering based approach. We show how to extract linguistic features from tweet data and to adapt TF-IDF weighting and word embeddings to the Turkish tweets. Since the model is based on linguistic features, it is language specific. The prediction model uses features applicable to Turkish language and related to writing style of Turkish Twitt...
Twitter Sentiment Analysis Experiments Using Word Embeddings on Datasets of Various Scales
Arslan, Yusuf; Kucuk, Dilek; Birtürk, Ayşe Nur (2018-06-15)
Sentiment analysis is a popular research topic in social media analysis and natural language processing. In this paper, we present the details and evaluation results of our Twitter sentiment analysis experiments which are based on word embeddings vectors such as word2vec and doc2vec, using an ANN classifier. In these experiments, we utilized two publicly available sentiment analysis datasets and four smaller datasets derived from these datasets, in addition to a publicly available trained vector model over ...
Online collaboration: Collaborative behavior patterns and factors affecting globally distributed team performance
Serce, Fatma Cemile; Swigger, Kathleen; Alpaslan, Ferda Nur; Brazile, Robert; Dafoulas, George; Lopez, Victor (2011-01-01)
Studying the collaborative behavior of online learning teams and how this behavior is related to communication mode and task type is a complex process. Research about small group learning suggests that a higher percentage of social interactions occur in synchronous rather than asynchronous mode, and that students spend more time in task-oriented interaction in asynchronous discussions than in synchronous mode. This study analyzed the collaborative interaction patterns of global software development learning...
Discourse Meaning: The View from Turkish
Zeyrek Bozşahin, Deniz; Özge, Umut (Mouton de Gruyter, 2020-05-01)
The volume aims to bring together original, unpublished papers on discourse structure and meaning from different frameworks or theoretical perspectives to address research questions revolving around issues instigated by Turkish. Another goal is to offer methodologically different solutions for the research gaps identified in individual chapters. The contributions are based on empirical generalizations and make use of, for example, computerized corpora as the data, examples compiled from naturally occurring ...
Transfer Learning Using Twitter Data for Improving Sentiment Classification of Turkish Political News
Kaya, Mesut; Fidan, Guven; Toroslu, İsmail Hakkı (2013-10-29)
In this paper, we aim to determine the overall sentiment classification of Turkish political columns. That is, our goal is to determine whether the whole document has positive or negative opinion regardless of its subject. In order to enhance the performance of the classification, transfer learning is applied from unlabeled Twitter data to labeled political columns. A variation of self-taught learning has been proposed, and implemented for the classification. Different machine learning techniques, including...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Tutaysalgir, P. Karagöz, and İ. H. Toroslu, “Clustering based personality prediction on turkish tweets,” presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Vancouver, Canada, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37038.