Sinir Ağı Dil Modelleri ve Evrensel Cümle Kodlayıcı Kullanarak Havayolu Müşteri Yorumlarının Duygu Analizi

Le résumé fourni par la source

Sentiment analysis represents a fundamental technique within the domain of natural language processing (NLP), employed for the purpose of discerning the emotional tenor of text-based data. Businesses may make strategic decisions by employing emotional insights derived from customer reviews to enhance customer satisfaction and improve service quality. In this study, a dataset comprising reviews from airline customers was employed. The dataset comprises the verification status, content, rating score, recommendation status, and sentiment analysis of each review, with a total of 1100 examples. The study examined four distinct models. The CNN-LSTM model was trained using a training-from-scratch strategy. Furthermore, two distinct neural network language models (Neural Network Language Model, NNLM) and the Universal Sentence Encoder (USE) were trained using a transfer learning approach. The CNN-LSTM model exhibited robust performance, achieving an accuracy rate of 92.06%. The nnlm-en-dim50 model achieved an accuracy rate of 90,87%, while the nnlm-en-dim128 model demonstrated a notably higher level of accuracy at 92.46%. The USE model exhibited the highest performance, with an accuracy rate of 95.63%. These findings suggest that deep learning and transfer learning techniques are effective tools for sentiment analysis. The study offers valuable insights into how businesses can utilize sentiment analysis technologies to enhance customer satisfaction and service quality. It is recommended that future studies investigate the performance of these models with different datasets and larger sample sizes.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.