Real-Time Lexicon-Based Sentiment Analysis Experiments On Twitter With A Mild (More Information, Less Data)

Date

2017-12-14

Author

Arslan, Yusuf
Birtürk, Ayşe Nur
Djumabaev, Bekjan
Kucuk, Dilek

Metadata

Show full item record

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

Item Usage Stats

218
views

0
downloads

Sentiment analysis of Twitter data is a well studied area, however, there is a need for exploring the effectiveness of real-time approaches on small data sets that only include popular and targeted tweets. In this paper, we have employed several sentiment analysis techniques by using dynamic dictionaries and models, and performed some experiments on limited but relevant datasets to understand the popularity of some terms and the opinion of users about them. The results of our experiments are promising.

Subject Keywords

Sentiment analysis, Social media, Data mining

URI

https://hdl.handle.net/11511/54952

Conference Name

IEEE International Conference on Big Data (IEEE Big Data)

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 ...
BIG DATA FOR INDUSTRY 4.0: A CONCEPTUAL FRAMEWORK Gökalp, Mert Onuralp; Kayabay, Kerem; Eren, Pekin Erhan; Koçyiğit, Altan (2016-12-17) Exponential growth in data volume originating from Internet of Things sources and information services drives the industry to develop new models and distributed tools to handle big data. In order to achieve strategic advantages, effective use of these tools and integrating results to their business processes are critical for enterprises. While there is an abundance of tools available in the market, they are underutilized by organizations due to their complexities. Deployment and usage of big data analysis t...
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...
End User Evaluation of the FAIR4Health Data Curation Tool Gencturk, Mert; Teoman, Alper; Alvarez-Romero, Celia; Martinez-Garcia, Alicia; Parra-Calderon, Carlos Luis; Poblador-Plou, Beatriz; Löbe, Matthias; Sinaci, A Anil (2021-05-27) The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method. We applied a series of questionnaires to a group of experts not only in different roles and skills, but also from various...
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 ...

Citation Formats

Y. Arslan, A. N. Birtürk, B. Djumabaev, and D. Kucuk, “Real-Time Lexicon-Based Sentiment Analysis Experiments On Twitter With A Mild (More Information, Less Data),” presented at the IEEE International Conference on Big Data (IEEE Big Data), Boston, MA, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54952.