Modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration

With the development of e-commerce, an increasing number of online reviews can serve as a promising data source for enterprises to improve online products. This paper proposes a method for modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Firstly, the attributes concerned by consumers are extracted from online reviews, and sentiment analysis of the extracted attributes is carried out using Standford CoreNLP. Secondly, to identify the types of product attributes, an improved Kano model is proposed based on the effects of product attributes on consumer total utility. On this basis, different attribute types are illustrated from the perspective of risk attitude. Then, the consumer aspirations are mined based on the risk attitudes of different attributes and the attribute impact on consumer satisfaction. According to the risk attitudes and aspirations of different attributes, the quantified satisfaction functions are constructed to provide more objective and accurate improvement suggestions. Finally, the proposed method is applied to the hotel service improvement to illustrate the effectiveness. First published online 13 April 202

Exploiting domain knowledge to enhance opinion mining using a hybrid semantic knowledgebase-machine learning approach

With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published on blogs, forums, and social networks. Online opinions play an important role in supporting consumers make decisions about purchasing products or services. In addition, customer reviews allow companies to understand the strengths and limitations of their products and services, which aids in improving their marketing campaigns. The challenge is that online opinions are predominantly expressed in natural language text, and hence opinion mining tools are required to facilitate the effective analysis of opinions from the unstructured text and to allow for qualitative information extraction. This research presents a Hybrid Semantic Knowledgebase-Machine Learning approach for mining opinions at the domain feature level and classifying the overall opinion on a multi-point scale. The proposed approach benefits from the advantages of deploying a novel Semantic Knowledgebase approach to analyse a collection of reviews at the domain feature level and produce a set of structured information that associates the expressed opinions with specific domain features. The information in the knowledgebase is further supplemented with domain-relevant facts sourced from public Semantic datasets, and the enriched semantically-tagged information is then used to infer valuable semantic information about the domain as well as the expressed opinions on the domain features by summarising the overall opinions about the domain across multiple reviews, and by averaging the overall opinions about other cinematic features. The retrieved semantic information represents a valuable resource for training a Machine Learning classifier to predict the numerical rating of each review. Experimental evaluation revealed that the proposed Hybrid Semantic Knowledgebase-Machine Learning approach improved the precision and recall of the extracted domain features, and hence proved suitable for producing an enriched dataset of semantic features that resulted in higher classification accuracy

ASPECT EXTRACTION PADA ULASAN MENGGUNAKAN PENGGABUNGAN LATENT DIRICHLET ALLOCATION DAN GLOBAL VECTOR FOR WORD REPRESENTATION

"Di era digital saat ini, semua informasi dapat ditemukan di berbagai jejaring sosial, seperti Facebook dan Twitter yang menjadi tempat beropini atau memberi ulasan. Biasanya masyarakat mengekspresikan ulasannya tidak secara keseluruhan tetapi hanya sebagian fitur saja pada setiap ulasan. Fitur dalam ulasan tersebut berisi aspek yang harus diekstraksi menggunakan aspek, dikumpulkan dalam beberapa kategori, dan dibagi menjadi polaritas yang berbeda. Sentiment Analysis berbasiskan aspek dapat membantu mengatasi hal tersebut. Aspect extraction merupakan task yang penting dalam pendekatan ini. Penelitian ini berfokus pada Aspect Extraction dan Latent Topic Identification menggunakan unsupervised learning. LDA (Latent Dirichlet Allocation) adalah pendekatan yang paling umum digunakan dalam unsupervised learning yang baik untuk menemukan topik dalam dokumen berukuran besar. Namun, LDA kurang efektif untuk melakukan aspect extraction terutama pada ulasan atau teks berukuran pendek karena mempengaruhi data sparsity, sehingga terjadi aspek dan topic yang tidak koheren dan tidak kompatibel. Untuk mengatasinya, kita mengusulkan LDA yang digabungkan dengan word embedding. GloVe (Global Vector for Word Representation) merupakan word embedding yang memiliki perpaduan kelebihan dari word embedding yang berdasarkan prediksi dan perhitungan. Pendekatan yang diajukan ini akan menguji pengaruh GloVe sebagai word embedding terhadap LDA sebagai topic modelling. Data penelitian menggunakan ulasan edukasi, e- Commerce, dan game. Data diolah menggunakan seleksi fitur dan dikelompokkan menggunakan LDA. Hasil pengujian menunjukkan bahwa penggabungan LDA- GloVe mempunyai nilai koheren yang tinggi daripada metode lain dengan peningkatan mencapai 79,6%. Hasil tersebut mengindikasikan bahwa word embedding mempunyai pengaruh yang signifikan terhadap LDA. Kata Kunci: Aspect Extraction, Review, Latent Dirichlet Allocation, GloVe