Publication
Content Matching and Sentiment Analysis
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | pt_PT |
| dc.contributor.advisor | Pinto, Filipe Marques da Silva Cabral | |
| dc.contributor.author | Rodrigues, Margarida Adriana Sampaio | |
| dc.date.accessioned | 2024-05-09T14:45:15Z | |
| dc.date.available | 2024-05-09T14:45:15Z | |
| dc.date.issued | 2024-03-01 | |
| dc.description.abstract | Developing new services or improving existing ones is becoming more accessible with the evolution of Natural Language Processing (NLP) techniques. Chatbots are a known example of an NLP-based service; they can interact with humans using text messages or natural language. NLP grants, however, the development of other types of services based on natural languages, such as machine translation, email spam detection, information extraction, content summarization, and question answering. A current need, to develop smart cities projects, is a system that can match content (text) from a project offer description with the candidates description by finding common patterns in different textual descriptions. This project presents an implementation of an automated tool with AI and NLP to match needs and concrete ideas for innovation with the skills and offers of the business sector, including start-ups and entrepreneurs. In sentiment analysis, NLP can be harnessed to recognize and categorize the emotional tone conveyed in textual content, such as project collaborator reviews, customer reviews, or social media posts. The sentiment analysis component in this project establishes a tool for comprehending and categorizing sentiments, for candidates seeking engagement in smart cities projects. | pt_PT |
| dc.identifier.tid | 203599675 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.19/8382 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Natural language processing | pt_PT |
| dc.subject | nlp | pt_PT |
| dc.subject | Content matching | pt_PT |
| dc.subject | Smart cities | pt_PT |
| dc.subject | Stemming and lemmatization | pt_PT |
| dc.subject | Bag-of-words | pt_PT |
| dc.subject | Bow | pt_PT |
| dc.subject | Term frequency-inverse document frequency | pt_PT |
| dc.subject | TFIDF | pt_PT |
| dc.subject | TF | pt_PT |
| dc.subject | IDF | pt_PT |
| dc.subject | Stop words | pt_PT |
| dc.subject | Cosine similarity | pt_PT |
| dc.subject | Flask | pt_PT |
| dc.subject | Sentiment analysis | pt_PT |
| dc.subject | Polarity | pt_PT |
| dc.title | Content Matching and Sentiment Analysis | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Engenharia Informática - Sistemas de Informação | pt_PT |
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