Recognition of Comparative Sentences from Online Reviews Based on Multi-feature Item Combinations

Zhang, Jie; Zheng, Liping; Zheng, Lijuan; Ge, Junyan

doi:10.1007/978-3-319-95933-7_23

Cited by 4 publications

(6 citation statements)

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“…For college management, you can also make full use of the emotions of "micro-content". (Zhang, Zheng, & Zheng, 2018) Emotion can reflect the personality and psychological state of the reviewer, so the school can grasp the psychological situation of the students in time, grasp the dynamics of the students in time, and is conducive to the student management work in colleges and universities.…”

Section: Precise Education Methods Based On Online Video Technology Tomentioning

confidence: 99%

A Mental Health Education Strategy Based on Online Videos

Jia¹,

Zhang²

2020

RACP

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To protect the mental health of college students, this paper proposes two methods based on online videos, namely, the accurate mental health education (AMHE) method and the personalized mental health education (PMHE) method. The AMHE analyzes the sequence and frequency of the access to category pages in the search for online videos, determines the categories of the videos watched by college students, and extracts the keywords from students' reviews on online videos. The PMHE examines the mental health problems of college students through data mining and big data analysis: the improved association rules algorithm is used to mine the access logs of online videos, the frequently accessed knowledge points are identified to determine the students' interests, and the relationship between knowledge points and interests is ascertained. The two methods were combined into a mental health education strategy, capable of adjusting the teaching contents flexibly and providing students with personalized services. This research sheds new light on the design of mental health education courses and the improvement of students' mental health.

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Section: Precise Education Methods Based On Online Video Technology Tomentioning

confidence: 99%

A Mental Health Education Strategy Based on Online Videos

Jia¹,

Zhang²

2020

RACP

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“…With regard to machine learning-based methods, investigated classifier algorithms include Naive Bayes [39], support vector machine (SVM) [40,41], decision tree [21], random forest [42], logistic regression [3], and bagging [3]. Input features generally include CSR-generated rules [39] and manually designed language features [3,41,43], such as keywords, sentiment, and dependency structures.…”

Section: Coarse-grained Classificationmentioning

confidence: 99%

A review for comparative text mining: From data acquisition to practical application

Wei

Zhao

Liu

et al. 2023

Journal of Information Science

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Social media provides customers with great opportunities to share their opinions regarding certain products and services. Comparative text, as an important expression form, deserves further exploration, since it contains considerable comparative information between different products and services. In this study, we review existing research on Comparative Text Mining (CTM) in the past 16 years. Basic concepts related to CTM are first described, and a general research framework is subsequently proposed. We then dive into each component of the research framework, ranging from data acquisition, comparative text identification (CTI), comparative relation extraction (CRE), to potential applications. In addition, we conduct extensive experimental analysis on existing methods for CTI and CRE, and clarify their limitations. Accordingly, we provide corresponding insights, and point out future research directions.

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“…Utilizing UGC on e-commerce platforms and social media for gaining comparative intelligence has attracted great attention in recent years. Prior related studies focused on two research directions: identifying comparative text (Ngo Xuan et al, 2015;Zhang et al, 2018), and mining comparative relations (Bi et al, 2019;Liu et al, 2019;Liu et al, 2020a;Liu et al, 2020b;Wang et al, 2020a), including competitors identifications (Liu et al, 2020a;Wang et al, 2020a) and competitive advantage analysis (Liu et al, 2019;Liu et al, 2020b). In this Section, we emphasize on reviewing research efforts on comparative text identification.…”

Section: Comparative Text Identificationmentioning

confidence: 99%

“…When implementing traditional machine learning methods for CTI, the CSR-generated rules generally serve as input features, supplemented with some keywords. The adopted algorithms include Naive Bayes (Jindal and Bing, 2006) and Support Vector Machine (SVM) (Zhang et al, 2018). These methods rely heavily on fixed sequence patterns and manually specified keywords, thus being time-consuming, ineffective at detecting implicit comparative text, and presenting poor generalization ability across domains.…”

Section: Comparative Text Identificationmentioning

confidence: 99%

“…One of its main challenges is that most of UGC does not convey comparative information. Therefore, comparative text identification from usergenerated content (CTI-UGC), whose purpose is to distinguish between comparative text and non-comparative text on e-commerce platforms and social media, is an important preliminary subtask, and has been an ongoing research (Ngo Xuan et al, 2015;Zhang et al, 2018). 3 However 4 , existing CTI-UGC systems have encountered great challenges from the perspective of both used methods and used data.…”

Section: Introductionmentioning

confidence: 99%

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