Orange Business Serv的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

俄羅斯社交媒體影響者對客戶行為的影響

為了解決Orange Business Serv的問題，作者葉亞登 這樣論述：

在今天的市場上，儘管競爭激烈，俄羅斯商業商店仍在繼續增長。說服影響者尋找替代工作以創辦新公司的原因尚未完全調查。在營銷組合和假設方面，本研究著眼於選擇影響者產品或服務的重要決策要素。驗證性因素分析（CFA）和結構方程模型也被用來確認本研究的信度和效度。根據調查結果，HI、H2、H3、H4、H5、H6、H7 和 H8 得到顯著支持（影響者形像對服務質量和品牌聲譽有影響，品牌聲譽對客戶承諾有影響，服務質量對服務質量有影響）對感知價值的影響，感知價值對品牌聲譽和客戶滿意度的影響，感知價值對品牌聲譽和客戶滿意度的影響，客戶滿意度對客戶承諾的影響，客戶承諾對客戶忠誠度的影響）在本研究中.這對於希望在新

市場開展新業務的影響者來說將非常有利。

Discriminative Linguistic Features Fusion with BERT for Aspectbased Sentiment Prediction of Airline Reviews

為了解決Orange Business Serv的問題，作者Duy-Duc Le Nguyen 這樣論述：

INTRODUCTION: Many customer reviews and comments evaluating a product or a service are shared on social media everyday. While an overall rating is mandatory, aspect ratings are optional. Understating customer opinions in various aspects helps airline companies improve their service during the COVID

-19 pandemic.OBJECTIVES: Developing a neural network model can identify aspect sentiments from a review text.METHODS: Three types of embedding methods are obtained: token embedding, positional embedding, and discriminative linguistic embedding. To get better input representation with its context, we

employ multi-head attention layers. Then, the classification token is used to predict aspect polarities.RESULTS: Our proposed model LiFeBERT achieves the best score in macro-average precision, recall, and f1-score on the TripAdvisor Airline Dataset, which has 190K reviews from the top 10 airline co

mpanies in 2019 and 2020. Also, customers are more unsatisfied with the airline services after the COVID-19 happening compare to the time before.CONCLUSION: This study reflects the importance of our discriminative linguistic embedding in the Transformer-based model in ABSA tasks. The LiFeBERT can be

applied to other domains such as hospitality and restaurant.