Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Zhang, Shaozhong; Zhong, Huihuang

doi:10.1109/access.2019.2893601

Cited by 42 publications

(24 citation statements)

References 48 publications

(73 reference statements)

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“…Aspect-level sentiment analysis involves two main steps: extracting the aspect from text data and then identifying the sentiment for each aspect, specifically positive or negative [13]. The most common data to be examined for its sentiment are social media data [14] and customer product reviews [15]. By analyzing sentiment, we can determine the users' and customers' preferences.…”

Section: Instance the Investigator Can Analyzementioning

confidence: 99%

Sentiment Analysis in a Forensic Timeline With Deep Learning

2020

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A forensic investigator creates a timeline from a forensic disk image after an occurrence of a security incident. This procedure aims to acquire the time for all events identified from the investigated artifacts. An investigator usually looks for events of interest by manually searching the timeline. One of the sources from which to build a timeline is log files, and these events are often found in log messages. In this paper, we propose a sentiment analysis technique to automatically extract events of interest from log messages in the forensic timeline. We use a deep learning technique with a context and content attention model to identify aspect terms and the corresponding sentiments in the forensic timeline. Terms with negative sentiments indicate events of interest and are highlighted in the timeline. Therefore, the investigator can quickly examine the events and other activities recorded within the surrounding time frame. Experimental results on four public forensic case studies show that the proposed method achieves 98.43% and 99.64% for the F1 score and accuracy, respectively.

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Section: Instance the Investigator Can Analyzementioning

confidence: 99%

Sentiment Analysis in a Forensic Timeline With Deep Learning

2020

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“…First, the extraction of sentiment nodes is performed through two steps, which are the extraction of entity words and the mining of entity-sentiment word pairs [44]. The entity-sentiment word pairs in the paper are called entities with attributes, and they are the keywords of sentiments to express various sentiments of consumer reviews on objects.…”

Section: Framework Of Sentiment Multiclassification Analysismentioning

confidence: 99%

“…We use a mutual information method to calculate the relation between an entity and its attribute. Mutual information of an entity word i e and an attribute word j a can be calculated by formula (8) [44]. a p e p a e p a e p a e MI 1 1…”

Section: B Mining Of Entities With Attributesmentioning

confidence: 99%

A Multiclassification Model of Sentiment for E-Commerce Reviews

Zhang

Zhong

et al. 2020

IEEE Access

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Consumer reviews are important information that reflects the quality of E-commerce goods and services and their existing problems after shopping. Due to the possible differences in consumers' experiences with goods and service quality, consumer reviews can involve multiple-aspect expressions of emotions or opinions. This may result in attitudes expressed by a consumer in the same review sometimes having a variety of emotions. We introduce a sentiment multiclassification method based on a directed weighted model. The model represents the sentiment entity vocabulary as the sentiment nodes and represents the relation between nodes as the directed weighted link. The sentiment entity vocabulary is the entity with attributes, which can express sentiment meaning in related reviews. Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and determined by the direct correlation calculation between them. The paths are all connected directed links from one node to another, which are composed of several nodes and links with close sentiment similarity. Then, we can establish a directed weighted model concerning the sentiments. Directed weighted links having similar sentiment relations with each other may constitute a directed weighted path. There are several directed weighted paths from a start node to the end nodes of the sentiment entity vocabulary in the directed weighted model. Each different path is a different sentiment expression, which represents a different sentiment type. The different sentiment classifications can be obtained through the restriction of path length. Experiments and analysis of the results show that the sentiment multiclassification model based on the directed weighted model proposed in this paper can classify the review sentiments according to different limited threshold rules. Comprehensive analysis indicates the classification results have good accuracy and high efficiency.

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“…A discrete model was constructed for computing social opinion by Wu et al [22] where voting-based approach was used for performing predictive mining analysis. The work of Zhang and Zhong [23] has carried out mining of the trust factor connected with two individual using sentiment analyses using shortest path. AskariSichani and Jalili [24] have investigated over complex network for formulating opinion using maximum-a-posteriori process considering identification of influential node.…”

Section: A Backgroundmentioning

confidence: 99%

Framework for opinion as a service on review data of customer using semantics based analytics

Rajeshwari¹,

Vagdevi²

2020

IJECE

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At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.

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Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Cited by 42 publications

References 48 publications

Sentiment Analysis in a Forensic Timeline With Deep Learning

Sentiment Analysis in a Forensic Timeline With Deep Learning

A Multiclassification Model of Sentiment for E-Commerce Reviews

Framework for opinion as a service on review data of customer using semantics based analytics

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