Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce

Customer reviews usually contain much information about one's online shopping experience. While positive reviews are beneficial to the stores, negative ones will largely influence consumers' decision and may lead to a decline in sales. Therefore, it is of vital importance to carefully and persuasively reply to each negative review and minimize its disadvantageous effect. Recent studies consider leveraging generation models to help the sellers respond. However, this problem is not well-addressed as the reviews may contain multiple aspects of issues which should be resolved accordingly and persuasively. In this work, we propose a Multi-Source Multi-Aspect Attentive Generation model for persuasive response generation. Various sources of information are appropriately obtained and leveraged by the proposed model for generating more informative and persuasive responses. A multi-aspect attentive network is proposed to automatically attend to different aspects in a review and ensure most of the issues are tackled. Extensive experiments on two real-world datasets, demonstrate that our approach outperforms the state-of-the-art methods and online tests prove that our deployed system significantly enhances the efficiency of the stores' dealing with negative reviews.Comment: Accepted at CIKM 2022 applied researc

Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management

Online customer feedback management plays an increasingly important role for businesses. Yet providing customers with good responses to their reviews can be challenging, especially as the number of reviews grows. This paper explores the potential of using generative AI to formulate responses to customer reviews. Using advanced NLP techniques, we generated responses to reviews in different authoring configurations. To compare the communicative effectiveness of AI-generated and human-written responses, we conducted an online experiment with 502 participants. The results show that a Large Language Model performed remarkably well in this context. By providing concrete evidence of the quality of AI-generated responses, we contribute to the growing body of knowledge in this area. Our findings may have implications for businesses seeking to improve their customer feedback management strategies, and for researchers interested in the intersection of AI and customer feedback. This opens opportunities for practical applications of NLP and for further IS research

Klasifikasi Ulasan Pengguna Aplikasi: Studi Kasus Aplikasi Ipusnas Perpustakaan Nasional Republik Indonesia (PNRI)

Menurunnya jumlah tren pengguna baru aplikasi iPusnas berpengaruh terhadap penurunan pencapaian nilai target laporan LKIP Pujasintara PNRI 2020 – 2024. Hal tersebut berkaitan dengan nilai peringkat ulasan pengguna aplikasi di Google Playstore yang dinilai masih lebih rendah dibandingkan aplikasi sejenis lainnya. Electronic Word of Mouth (EWOM) yang sangat berpengaruh terhadap keputusan calon pengguna baru aplikasi dalam mempertimbangkan aplikasi terbaik yang sejenis, karena melibatkan tinjauan nilai peringkat dan ulasan pengguna. Beberapa penelitian terdahulu membuktikan bahwa kesulitan selalu dihadapi ketika melakukan analisis atau penggalian informasi penting dalam ulasan pengguna aplikasi secara manual. Analisis ulasan sangat berguna untuk mengembangkan fitur layanan aplikasi agar dapat meningkatkan kepuasan pengguna dan peringkat nilai aplikasi, sehingga diperlukan alat bantu klasifikasi ulasan pengguna secara otomatis dengan mencari model terbaik yang sesuai. Penelitian ini menerapkan metodologi CRISP-DM, tetapi hanya sampai tahap evaluasi. Algoritma klasifikasi yang digunakan adalah Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), serta kombinasi fitur tf-idf unigram, bigram, dan trigram. Hasil penelitiannya adalah kombinasi fitur tf-idf unigram (F1) dengan algoritma SVM mencapai nilai terbaik untuk setiap nilai evaluasi precision, recall, dan f1-score masing-masing sebesar 87%. Nilai evaluasi terendah precision 55% dari hasil kombinasi fitur F2 dengan SVM, recall 42% dan f1-score 32% dari kombinasi fitur F3 dengan logistic regression

Supporting Online Customer Feedback Management with Automatic Review Response Generation

The growing amount of online reviews plays a significant role in a business' image and performance. Businesses in the hospitality industry often lack necessary resources to organize and manage online customer feedback and are therefore likely to search for alternative ways to handle this. AI-based technologies may offer valuable solutions. However, there is currently little research on if and how AI solutions may support the process of responding to online customer feedback in the hospitality industry. This paper presents and evaluates a concept for assisting customer feedback management with automatically generated responses to online reviews. Our solution contributes to ongoing investigations into text generation applications for supporting human authors and also proposes new approaches and potential business models for managing online customer feedback