Tổng quan và so sánh thực nghiệm các mô hình xử lý ngôn ngữ tự nhiên (NLP) cùng với việc giới thiệu và ứng dụng thực nghiệm các mô hình tự động mã hóa trong marketing

Springer Science and Business Media LLC - Tập 50 - Trang 1324-1350 - 2022
Venkatesh Shankar1, Sohil Parsana1,2
1Center for Retailing Studies, Mays Business School, College Station, USA
2Oracle, College Station, USA

Tóm tắt

Với trí tuệ nhân tạo thâm nhập vào các cuộc trò chuyện và tương tác marketing thông qua công nghệ và truyền thông kỹ thuật số, các mô hình học máy, đặc biệt là các mô hình xử lý ngôn ngữ tự nhiên (NLP), đã gia tăng phổ biến trong việc phân tích dữ liệu không có cấu trúc trong marketing. Tuy nhiên, chúng ta vẫn chưa hiểu rõ các mô hình NLP nào phù hợp với những ứng dụng marketing nào và những hiểu biết nào có thể được rút ra tốt nhất từ chúng. Chúng tôi xem xét các mô hình NLP khác nhau và các ứng dụng của chúng trong marketing. Chúng tôi nêu ra những lợi ích và hạn chế của các mô hình này và làm nổi bật các điều kiện dưới đó các mô hình khác nhau là phù hợp trong bối cảnh marketing. Chúng tôi giới thiệu các mô hình NLP tự động mã hóa thần kinh mới nhất, trình bày các mô hình này để phân tích các thông báo sản phẩm mới và các bài báo tin tức, và cung cấp một so sánh thực nghiệm giữa các mô hình tự động mã hóa khác nhau cũng như mô hình NLP thống kê. Chúng tôi thảo luận về những hiểu biết từ sự so sánh này và cung cấp hướng dẫn cho các nhà nghiên cứu. Chúng tôi phác thảo các mở rộng tương lai của các mô hình NLP trong marketing.

Từ khóa

#Xử lý ngôn ngữ tự nhiên #mô hình tự động mã hóa #học máy #marketing #phân tích dữ liệu không có cấu trúc.

Tài liệu tham khảo

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