Sentiment analysis application in automated profiling in the financial services sector

Moauro, Filippo (A.A. 2020/2021) Sentiment analysis application in automated profiling in the financial services sector. Tesi di Laurea in Customer intelligence & big data, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 105. [Master's Degree Thesis]

Full text for this thesis not available from the repository.


Artificial intelligence implementations in data analysis, machine learning and sentiment analysis. Data mining and its importance in taking decisions. Machine learning and main data mining techniques. Growth of data volumes and types and new machine learning applications. Text mining and its numerous applications. Comparison between data mining and text mining. Overview on sentiment analysis. Consumer choice architecture and the necessity for consumer profiling. Choice architecture and theories of choice. Sources of influence for consumers. Consumer profiling, benefits and concerns. Banking and financial situation in Italy. First developments and the quasi-banking activity. The establishment of modern banking. The financial sector nowadays. The financial habits in Italy. The 3 pillars of the Italian pension system. Predictive model for consumer profiling enriched by text mining and sentiment analysis data. Presentation of the project: objectives and methodology. The data collection process. Integration of the data sources, data preparation, and data labelling. Analysis of the client base in the modelling dataset. The predictive models.


Bibliografia e sitografia: pp. 70-72.

Thesis Type: Master's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Master's Degree Programs > Master's Degree Program in Marketing (LM-77)
Chair: Customer intelligence & big data
Thesis Supervisor: Italiano, Giuseppe Francesco
Thesis Co-Supervisor: Laura, Luigi
Academic Year: 2020/2021
Session: Autumn
Deposited by: Alessandro Perfetti
Date Deposited: 17 May 2022 10:49
Last Modified: 17 May 2022 10:49


Downloads per month over past year

Repository Staff Only

View Item View Item