2018
Twanabasu, Bikesh
Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques Masters Thesis
Departamento de Lenguajes y Sistemas Informáticos, Castellón, 2018.
BibTeX | Tags: machine learning, Mastergeotech, sentiment analysis
@mastersthesis{Twanabasu2018,
title = {Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques},
author = {Bikesh Twanabasu},
editor = {Francisco Ramos-Romero and Óscar Belmonte-Fernández and Roberto Henriques (supervisor)},
year = {2018},
date = {2018-03-02},
address = {Castellón},
school = {Departamento de Lenguajes y Sistemas Informáticos},
keywords = {machine learning, Mastergeotech, sentiment analysis},
pubstate = {published},
tppubtype = {mastersthesis}
}
2016
Ramos-Romero, Francisco; Huerta-Guijarro, Joaquín; Gómez, Aaron
Picfeel: merging geolocation pictures and emotions Proceedings Article
In: AGILE 2016. Helsinki 14-17 June 2016, Helsinki, 2016.
Abstract | Links | BibTeX | Tags: geocoding services, geolocation, mobile apps, sentiment analysis, STARTUJI-2015
@inproceedings{RamosRomero2016,
title = {Picfeel: merging geolocation pictures and emotions},
author = { Francisco Ramos-Romero and Joaquín Huerta-Guijarro and Aaron Gómez},
url = {https://agile-online.org/Conference_Paper/cds/agile_2016/posters/186_Paper_in_PDF.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {AGILE 2016. Helsinki 14-17 June 2016},
address = {Helsinki},
abstract = {Sentiment analysis enables us to identify and extract subjective information of certain resources. With this aim, it is often used language processing techniques, textual analysis or computational linguistics. However, this process is quite expensive in terms of computational cost and the results obtained so far are relatively difficult to obtain or understand or even impossible to detect such as irony in textual analysis. In this work, we aimed at obtaining this information directly from the users. Specifically, we created a mobile app that allows us to take a picture and select the emotion it causes to the user, and then we automatically located it, allowing us to create a worldmap of emotions.},
keywords = {geocoding services, geolocation, mobile apps, sentiment analysis, STARTUJI-2015},
pubstate = {published},
tppubtype = {inproceedings}
}
Sentiment analysis enables us to identify and extract subjective information of certain resources. With this aim, it is often used language processing techniques, textual analysis or computational linguistics. However, this process is quite expensive in terms of computational cost and the results obtained so far are relatively difficult to obtain or understand or even impossible to detect such as irony in textual analysis. In this work, we aimed at obtaining this information directly from the users. Specifically, we created a mobile app that allows us to take a picture and select the emotion it causes to the user, and then we automatically located it, allowing us to create a worldmap of emotions.