Opinion Analysis of Product Reviews

Zhang, Shu; Jia, Wenjie; Xia, Yingju; Meng, Yan; Yu, Hongfang

doi:10.1109/fskd.2009.200

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…From the perspective of supervised methods, Jin et al (2009) used the hidden Markov model and bootstrapping algorithm to extract camera attributes from online reviews; they argued that their proposed strategy could recognize new features without relying on natural language processing technologies. Zhang et al (2009) and Xu et al (2011) used conditional random fields, domain dictionary and language features to extract product aspects, but it takes more time to construct and update the domain dictionary. Moreover, topic models (Andrzejewski et al, 2009;Xu et al, 2015) and real-time review streams (Dragoni et al, 2019) have also been used in attributes extraction.…”

Section: Attribute Extractionmentioning

confidence: 99%

Exploring the distribution regularities of user attention and sentiment toward product aspects in online reviews

Qin

Zhang

2021

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Purpose To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews. Design/methodology/approach Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper. Findings The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product. Research limitations/implications This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms. Originality/value This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

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Section: Attribute Extractionmentioning

confidence: 99%

Exploring the distribution regularities of user attention and sentiment toward product aspects in online reviews

Qin

Zhang

2021

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“…In a machine learning-based attribute extraction method, Zhang et al [13] introduced word-level features in the CRF model and used domain dictionary knowledge as an aid for product attribute extraction. Xu et al [14] introduced shallow syntactic information and heuristic location information and input them to CRF as features, which effectively improved the attribute extraction performance of the model.…”

Section: Related Workmentioning

confidence: 99%

“…The loss value calculation formula is shown in Equation (13), where loss start and loss end represent the loss values of entity head recognition and entity tail recognition, respectively, and loss attribute represents the loss value generated by the attribute sequence labelling. α, β, γ ∈ ½0, 1 are hyperparameters that control the weighted summation of the threeloss values.…”

Section: Loss Value Calculationmentioning

confidence: 99%

Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

You

Yang

Han³

et al. 2021

Wireless Communications and Mobile Computing

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The effective organization and utilization of military equipment data is an important cornerstone for constructing knowledge system. Building a knowledge graph in the field of military equipment can effectively describe the relationship between entity and entity attribute information. Therefore, relevant personnel can obtain information quickly and accurately. Attribute extraction is an important part of building the knowledge graph. Given the lack of annotated data in the field of military equipment, we propose a new data annotation method, which adopts the idea of distant supervision to automatically build the attribute extraction dataset. We convert the attribute extraction task into a sequence annotation task. At the same time, we propose a RoBERTa-BiLSTM-CRF-SEL-based attribute extraction method. Firstly, a list of attribute name synonyms is constructed, then a corpus of military equipment attributes is obtained through automatic annotation of semistructured data in Baidu Encyclopedia. RoBERTa is used to obtain the vector encoding of the text. Then, input it into the entity boundary prediction layer to label the entity head and tail, and input the BiLSTM-CRF layer to predict the attribute label. The experimental results show that the proposed method can effectively perform attribute extraction in the military equipment domain. The F 1 value of the model reaches 77% on the constructed attribute extraction dataset, which outperforms the current state-of-art model.

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“…Liu [6][7] identifies product features using association mining firstly, and then adjective word nearest to product features is regarded as opinion word. Zhang [8] adopt the CRFs model to find product features with the domain knowledge. Our research is closely related with [3][6] [8], but it differs form others in that we use chunk features and heuristic information to identify product features for CRFs.…”

Section: Related Workmentioning

confidence: 99%

Product features mining based on Conditional Random Fields model

Zhao

Zheng

et al. 2010

2010 International Conference on Machine Learning and Cybernetics

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Opinion mining has become a hot issue attracting the attention of many researchers recently, in which the opinion feature is essential to its modeling. In the opinion mining of products, opinion feature identification is to mine product features from product reviews. In this paper, we present a Conditional Random Fields model based Chinese product features identification approach, integrating the chunk features and heuristic position information in addition to the word features, part-of-speech features and context features. Experiments show that the proposed techniques effectively improve the performance of product opinion mining.

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Opinion Analysis of Product Reviews

Cited by 8 publications

References 8 publications

Exploring the distribution regularities of user attention and sentiment toward product aspects in online reviews

Exploring the distribution regularities of user attention and sentiment toward product aspects in online reviews

Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

Product features mining based on Conditional Random Fields model

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