Detecting User Emotions in Twitter through Collective Classification

Date

2016-11-11

Author

İLERİ, İBRAHİM
Karagöz, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

171
views

0
downloads

The explosion in the use of social networks has generated a big amount of data including user opinions about varying subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this study, a different user-centric approach for emotion detection is considered such that connected users may be more likely to hold similar emotions; therefore, leveraging relationship information can complement emotion inference task in social networks. Employing Twitter as a source for experimental data and working with the proposed collective classification algorithm, emotions of the users are predicted in a collaborative setting.

Subject Keywords

Social networks, Emotion analysis, Sentiment analysis, Collective classification

URI

https://hdl.handle.net/11511/35621

DOI

https://doi.org/10.5220/0006037502050212

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Collective classification of user emotions in twitter İleri, İbrahim; Karagöz, Pınar; Department of Computer Engineering (2015) The recent explosion of social networks has generated a big amount of data including user opinions about varied subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Because of the fact that many different emotions are needed to be dealt with at this point, the problem becomes much more complicated. In this thesis, a different user-centric approach is ...
Understanding IMF Decision-Making with Sentiment Analysis Deniz, Ayca; Angin, Merih; Angın, Pelin (2022-01-01) With the advances in information technologies, the amount of available data on web sources where people express their opinions increases continually. Sentiment analysis is one of the effective tools for decision-makers to gain insights from massive heaps of data. The field of International Organizations, which produces big data in the form of large documents, has significant potential to benefit from sentiment analysis in decision-making. In this paper, we evaluate the effectiveness of different sentiment a...
Named entity recognition from scratch on social media Önal, Kezban Dilek; Karagöz, Pınar (null; 2015-09-07) With the extensive amount of textual data flowing through social media platforms, the interest in Information Extraction (IE) on such textual data has increased. Named Entity Recognition (NER) is one of the basic problems of IE. State-of-the-art solutions for NER face an adaptation problem to informal texts from social media platforms. In this study, we addressed this generalization problem with the NLP from scratch idea that has been shown to be successful for several NLP tasks on formal text. Experimental...
Identifying textual personal information using bidirectional LSTM networks Ertekin Bolelli, Şeyda (2018-07-09) Data-driven approaches based on the data collected from individuals are improving everyday life as a result of the developments in big data studies. Prior to developing such an approach, removal of personal information from the data is important since personal information contained in data would jeopardize people's privacy and may harm related individuals. Especially in the field of health sciences, identifying personal information in the collected data is a difficult task as most of the data collected in h...
Learning to Rank for Joy Orellana-Rodriguez, Claudia; Nejdl, Wolfgang; Diaz-Aviles, Ernesto; Altıngövde, İsmail Sengör (2014-04-11) User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking. To this end, we follow a learning to rank approach, which allows us to compare the performance of different sets of features when the ranking task goes beyond mere relevance and requires an affective understanding of the videos. Our results show that, while b...

Citation Formats

İ. İLERİ and P. Karagöz, “Detecting User Emotions in Twitter through Collective Classification,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35621.