johnnatan-messias / monitorlog

MonitorLog - http://johnnatan.me

HOW TO RUN

Import the project into the newest Android Studio (https://developer.android.com/studio)

MORE INFORMATION ABOUT OUR WORK:

Our team cares about the scientific reproducity. Therefore, aiming at allowing reproducibility we release the code to the research community.

More details and explanations may be found on our Asonam’16 paper: Towards sentiment analysis for mobile devices Johnnatan Messias and João P. Diniz and Elias Soares and Miller Ferreira and Matheus Araújo and Lucas Bastos and Manoel Miranda and Fabrício Benevenuto 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Please properly cite our work if you find it useful

@INPROCEEDINGS{messias2016asonam, author={Johnnatan Messias and Joao P. Diniz and Elias Soares and Miller Ferreira and Matheus Araujo and Lucas Bastos and Manoel Miranda and Fabricio Benevenuto}, booktitle={2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, title={Towards sentiment analysis for mobile devices}, year={2016}, pages={1390-1391}, abstract={The increasing use of smartphones to access social media platforms opens a new wave of applications that explore sentiment analysis in the mobile environment. However, there are various existing sentiment analysis methods and it is unclear which of them are deployable in the mobile environment. This paper provides the first of a kind study in which we compare the performance of 17 sentence-level sentiment analysis methods in the mobile environment. To do that, we adapted these sentence-level methods to run on Android OS and then we measure their performance in terms of memory usage, CPU usage, and battery consumption. Our findings unveil sentence-level methods that require almost no adaptations and run relatively fast as well as methods that could not be deployed due to excessive use of memory. We hope our effort provides a guide to developers and researchers interested in exploring sentiment analysis as part of a mobile application and can help new applications to be executed without the dependency of a server-side API.}, keywords={Android (operating system);application program interfaces;mobile computing;sentiment analysis;smart phones;social networking (online);storage management;Android OS;CPU usage;battery consumption;memory usage;mobile devices;sentiment analysis;server-side API;smartphones;social media platforms;Batteries;Mobile communication;Performance evaluation;Random access memory;Sentiment analysis;Smart phones}, doi={10.1109/ASONAM.2016.7752426}, month={Aug},}