Mesure de la satisfaction de clients basée sur les Commentaires en ligne
| dc.contributor.author | Himeur Younes HadjadjDhiaaElhak | |
| dc.date.accessioned | 2024-09-19T09:20:26Z | |
| dc.date.available | 2024-09-19T09:20:26Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Have you ever wondered how to use the mass of information we have today? An example is the online news feed, online store reviews or social media. The problem is that this information is often so abundant that it becomes overflowing and virtually unusable. With over 6,000 tweets per second, we're probably missing a ton of information and losing the edge over those who can use it. By combining text mining, natural language processing and machine learning, it becomes possible to create an application capable of analyzing the feeling of a text at the speed of light. Capturing such a feeling is called sentiment analysis, and that is what we are trying to achieve in this project. The objective of this project is to go through all the steps necessary to develop a machine learning model capable of analyzing and classifying any customer sentiment from a text review, in theory and in practice with Python. We will see how to clean and transform this textual data, so that it can be used efficiently by a model. We will finally build our model and test it with real customer reviews. Now think what we can learn if we can extract all of this useful information from this text in a reasonable amount of time, which is what we are going to try to do in this project. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/6639 | |
| dc.language.iso | fr | |
| dc.title | Mesure de la satisfaction de clients basée sur les Commentaires en ligne | |
| dc.type | Thesis |