Analysis processes treatment study information in sentiment analysis using technology Google
Article Sidebar
Main Article Content
Abstract
In recent years, Big Data has made its way amongst the main market analysis tools, linking itself to machine learning techniques in order to learn about the data owned. One of the fastest growing areas is natural language processing, which provides the researcher with data on text structures and meanings. In order to deep in into this area, Google has created the natural language API, allowing researchers to work with different aspects of language functions, including sentiment analysis, providing information on the predominant emotional response to a previously selected content, and allowing it to obtain a score that analyzes the valence of emotions with dichotomous values. The object of this study is to analyze the different processes that a researcher has to use to obtain useful information for their research. From the extraction of information to obtaining data that helps the researcher to draw conclusions, a long process of information processing is developed. The study will show us how the various tools available to Google on its own Google Cloud Platform provide a researcher with the necessary support for the development of their work, once the information to be analyzed is already available. In addition, it will be complemented with tracking tools to extract the desired text, depending on where it is.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The main author must deliver the letter of transfer of copyright, according to the model provided by Vivat Academia, Revista de comunicación, which declares the transfer of copyright to the journal and make explicit the rights of authors regarding the dissemination and use of the manuscript once published.
Creative Commons Attribution/Non Commercial 4.0 International
References
Agavanakis, K. N., Karpetas., G. E., Taylor. M., Pappa, E., Michail, C. M., Filos, J., Trachana, V., & Kontopoulou, L. (2019). Practical machine learning based on cloud computing resources. AIP Conference Proceedings, 2123. https://doi.org/10.1063/1.5117023 DOI: https://doi.org/10.1063/1.5117023
Alcover de la Hera, C. M. (2008). Neurociencia social: hacia la integración de las explicaciones sociales y biológicas de la conducta social. Método, teoría e investigación en Psicología social. 187-214. Pearson Educación.
Boubela, R. N., Kalcher, K., Huf, W., Našel, C., & Moser, E. (2016). Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project. Frontiers in Neuroscience, 9(JAN), 492. https://doi.org/10.3389/fnins.2015.00492 DOI: https://doi.org/10.3389/fnins.2015.00492
Challita, S., Zalila, F., Gourdin, C., & Merle, P. (2018). A precise model for Google cloud platform. Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018, 177–183. https://doi.org/10.1109/IC2E.2018.00041 DOI: https://doi.org/10.1109/IC2E.2018.00041
Eklund, A., Andersson, M., & Knutsson, H. (2012). FMRI analysis on the GPU-Possibilities and challenges. Computer Methods and Programs in Biomedicine, 105(2), 145–161. https://doi.org/10.1016/j.cmpb.2011.07.007 DOI: https://doi.org/10.1016/j.cmpb.2011.07.007
Harmon-Jones, E., & Beer Jennifer. (2009). Methods in Social Neuroscience. The Guilford Press.
Liu, X., Hao, L., & Yang, W. (2019). Bigeo: A foundational PaaS framework for efficient storage, visualization, management, analysis, service, and migration of geospatial big data—a case study of Sichuan province, China. ISPRS International Journal of Geo-Information, 8(10). https://doi.org/10.3390/ijgi8100449 DOI: https://doi.org/10.3390/ijgi8100449
Mart, E., Mart, M. T., y Ure, L. A. (2014). Desafíos del Análisis de Sentimientos. October. V Jornadas TIMM. 61–63.
Martínez Herrador, J. L., Núñez Cansado, M., y Valdunquillo Carlón, M. I. (2020). Metodología de neuromarketing: medición de Sociograph aplicada al análisis de la narrativa audiovisual erótica y sus aplicaciones a la estrategia de mercadotecnia. Vivat Academia. 131–153. https://doi.org/10.15178/va.2020.150.131-153 DOI: https://doi.org/10.15178/va.2020.150.131-153
Poggi, N., Berral, J. L., Fenech, T., Carrera, D., Blakeley, J., Minhas, F., & Vujic, N. (2016). The state of SQL-on-Hadoop in the Cloud. 4th IEEE International Conference on Big Data (Big Data), 1432–1443. https://doi.org/10.1109/BigData.2016.7840751 DOI: https://doi.org/10.1109/BigData.2016.7840751
Quintana, Á. (2019). Contrastando herramientas del Proceso del Lenguaje Natural con encuestas. La nueva comunicación del siglo XXI. Pirámide
Singh, J., Gill, R., & Goyal, G. (2019). Extracting and understanding user sentiments for big data analytics in big business brands. In Big Data Recommender Systems - Volume 2: Application Paradigms. Institution of Engineering and Technology. 235-257. Institution of Engineering and Technology. DOI: https://doi.org/10.1049/PBPC035G_ch13
Yuhanna, N. (2020). The Forrester WaveTM: Data Management For Analytics, Q1 2020 The 14 Providers That Matter Most And How They Stack Up Key Takeaways. The Forrester https://reprints.forrester.com/#/assets/2/157/RES157286/reports