openIn an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent.
Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks.
The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction.
The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself.
This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis.
Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all.
Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task.
During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis.
Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production.
The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment.In an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent.
Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks.
The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction.
The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself.
This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis.
Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all.
Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task.
During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis.
Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production.
The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment
