Tracking Topic Trends for Short Texts

He, Liyan; Du, Yajun; Ye, Yongtao

doi:10.1007/978-981-10-7359-5_12

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Y. Zhu et al [25] focused on two tasks: making use of different external corpus to identify topics from texts and adding the weight of a few features in texts. L. He et al [26] presented a novel model for short texts, referred to as the Topic Trend Detection (TTD) model. This model derived more typical terms to represent the topics found in short texts and improved the coherence of topic representations.…”

Section: Short Text Topic Mining Methodsmentioning

confidence: 99%

Improved LDA Model for Credibility Evaluation of Online Product Reviews

Wang

Zhang

Huang

et al. 2019

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

When individuals make a purchase from online sources, they may lack first-hand knowledge of the product. In such cases, they will judge the quality of the item by the reviews other consumers have posted. Therefore, it is significant to determine whether comments about a product are credible. Most often, conventional research on comment credibility has employed supervised machine learning methods, which have the disadvantage of needing large quantities of training data. This paper proposes an unsupervised method for judging comment credibility based on the Biterm Sentiment Latent Dirichlet Allocation (BS-LDA) model. Using this approach, first we derived some distributions and calculated each comment's credibility score via them. A comment's credibility was judged based on whether it achieved a threshold score. Our experimental results using comments from Amazon.com demonstrated that the overall performance of our approach can play an important role in determining the credibility of comments in some situation.

show abstract

Section: Short Text Topic Mining Methodsmentioning

confidence: 99%