Mining customer product reviews for product development: A summarization process

Hou, Tianjun; Yannou, Bernard; Leroy, Yann; Poirson, Émilie

doi:10.1016/j.eswa.2019.04.069

Cited by 52 publications

(28 citation statements)

References 41 publications

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“…These words keep getting closer or keep moving away as per the passage of time. This technique is a useful method for topic or subject assessment [17], [33], [48]. Table 3 shows the comparison of sentence score and Gensim-Word2Vec abstractive summaries.…”

Section: E Word Embeddings and Text Summarizationmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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Section: E Word Embeddings and Text Summarizationmentioning

confidence: 99%

“…The second approach of crucial line extraction has two other methods. In the first approach, the sentence score algorithm extracts the high score text lines [39], [48]. In the second approach, the word embeddings algorithm extracts those lines with a high frequency of mutually occurring words.…”

Section: B Making Multiple Summarized Parallel Corpusesmentioning

confidence: 99%

Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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“…Therefore, exploiting UGC has been a very efficient and reliable way of customer hearing. Many researchers have paid attention to this application in recent years (Zhu et al, 2011;Eirinaki et al, 2012;Serrano-Guerrero et al, 2015;Balazs and Velasquez, 2016;Hou et al, 2019;Zhang et al, 2019), and sentiment analysis is recognized as a powerful tool (Bagheri et al, 2013;Wang and Wang, 2014;Poria et al, 2016b;Mirtalaie et al, 2018Mirtalaie et al, , 2019. Coarse-grained sentiment analysis detects sentiments toward a subject as a whole.…”

Section: Introductionmentioning

confidence: 99%

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

Zhu

Jin

et al. 2021

AIEDAM

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With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.

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“…The exponential growth of Web 2.0 has dramatically impacted the evolution of e-commerce platforms [1][2][3][4]. On the one hand, some recent statistics show that 72% of customers will not take action until they read reviews, and only 6% of consumers don't trust customer reviews at all, on the other hand, the number of usergenerated reviews attached to an online entity could easily exceed thousands [5,6].…”

Section: Introductionmentioning

confidence: 99%

MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

Benlahbib

Nfaoui

2021

IJECE

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Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.

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Mining customer product reviews for product development: A summarization process

Cited by 52 publications

References 41 publications

Unstructured Text Documents Summarization With Multi-Stage Clustering

Unstructured Text Documents Summarization With Multi-Stage Clustering

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

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