An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

Liu, Chen; Tang, Li; Shan, Wei

doi:10.3390/su10051425

Cited by 11 publications

(12 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lei et al (2010) calculate the importance of candidate feature words by building a bipartite graph between candidate feature words and emotional words and utilizing the hyperlink-induced topic search (HITS) algorithm of webpage ranking. Liu et al (2018) combine PMI and a weighted HITS algorithm to sort candidate feature words on this basis. Meanwhile, Yan et al (2015) build a network structure of "feature words emotional words" based on a dependency syntactic analysis, and then apply the PageRank algorithm for feature extraction.…”

Section: Feature Extractionmentioning

confidence: 99%

Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis

Wan

Peng

Wang

et al. 2021

INTR

View full text Add to dashboard Cite

PurposeThis paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.Design/methodology/approachIn Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.FindingsThe study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.Originality/valueThis research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis

Wan

Peng

Wang

et al. 2021

INTR

View full text Add to dashboard Cite

show abstract

“…For example, Alrababah et al (2017) first identify explicit opinionated features using WordNet (Miller, 1995;Thelwall et al, 2010) and sentiment strength estimator SentiStrength, then remove irrelevant ones according to frequency and semantic knowledge. Liu et al (2018) first select candidate feature-sentiment pairs by dependency syntax analysis, then filter them using pointwise mutual information (PMI; Church and Hanks, 1990) with a weighted hyperlink-induced topic search (HITS) algorithm. After feature identification, sentiment estimation can be carried out with sentiment lexicons, like SentiStrength and SentiWordNet (Baccianella et al, 2010).…”

Section: Fine-grained Sentiment Analysismentioning

confidence: 99%

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

Zhu

Jin

et al. 2021

AIEDAM

View full text Add to dashboard Cite

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.

show abstract

“…Such evaluations give support only to solutions which detect needs already addressed in products and therefore are not fit for the evaluation of NPD. Other studies compare their approaches to a list of gold standard needs generated by having annotators read through a subset of the UGC posts used in the analysis [5][6][7][8][9]. Although these studies have other uses (e.g.…”

Section: Introductionmentioning

confidence: 99%

The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

Kilroy,

Caton,

Healy

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

In recent years, there have been many studies which summarize User Generated Content as lists of ranked keyphrases representing customer needs for the purposes of New Product Development. However, methods for the evaluation of keyphrase lists do not robustly assess solutions for these purposes. Therefore, in this paper we present the "Trending Customer Needs" (TCN) dataset of over 9000 top trending customer need keyphrases organized by month from 2007-2021 which spans 37 product categories in the area of Consumer Packaged Goods (e.g. toothpaste, eyeliner, beer etc.). TCN is a curated dataset for the benchmarking of supervised machine learning approaches in the prediction of customer needs using User Generated Content. We describe the process of curating TCN while ensuring its quality. Finally, we demonstrate its utility via a case study of Reddit discourse as a potential predictor for future customer needs in Consumer Packaged Goods.

show abstract

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

Cited by 11 publications

References 38 publications

Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis

Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis

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

The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

Contact Info

Product

Resources

About