Browsing by Author "Pereira, Isadora Manuel Almeida"
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- Geração de relatórios a partir da análise de avaliações de unidades hoteleirasPublication . Pereira, Isadora Manuel Almeida; Faria, Luiz Felipe Rocha deNowadays, customer reviews play a vital role in determining the success of businesses, particularly in the hospitality industry, where online feedback is both abundant and influential. However, the high volume of reviews presents a challenge for hotel owners and managers, who need efficient ways to extract useful insights. This dissertation addresses this issue by developing the FeedbackFunnel, a Natural Language Processing (NLP) model capable of analysing and summarizing customer reviews to provide concise and meaningful information. The model integrates three components: sentiment analysis, feature synthesis, and multidocument summarization. Each component was rigorously tested and improved individually to enhance performance. The Sentiment analysis was conducted using logistic regression combined with a TFIDF unigram model, chosen for its effectiveness in accurately classifying sentiments. The Feature synthesis for sentence creation component synthesized key features from sentiment analysis into sentences, summarizing the most notable positive and negative aspects of the reviews. For the summarization component, the pre-trained “sshleifer/distilbart-cnn-6-6” model was used to generate concise summaries from multiple reviews. To validate the performance of the models, traditional metrics such as accuracy were used for sentiment analysis, while more advanced measures like embedding-based similarity scores and perplexity were employed to assess the quality and coherence of the generated summaries. The developed model produced promising results by effectively capturing both positive and negative aspects mentioned in the review, even when the general sentiment leaned in one direction. However, there are still areas that can be improved. Enhancing the sentence creation component by using a pre-trained model to generate sentences could improve the coherence and richness of the generated content, moving beyond the current rigid and simplistic structure. Additionally, fine-tuning the summarization component on a domain-specific dataset could significantly improve the model’s performance.