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Sentiment analysis of product reviews has now become one of the important research directions in the field of NLP, which is of great significance in helping merchants better understand user preferences and providing decision-making basis for other users to purchase products. Existing product review sentiment analysis models based on deep learning m...
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... order to verify the effectiveness of the model, the ALBERT-LSTM model is compared with the ALBERT model and the LSTM model. The experimental results are shown in Table 3. Comparing the classification results of the above three models, it is found that the ALBERT-LSTM model proposed in this paper achieves better performance than a single ALBERT model and LSTM model in all three indicators. ...Similar publications
In this comparative study, we investigated different influential factors of mobile-assisted language learning (MALL) for improving productive and receptive language learning skills. To do this, 100 scientific research papers were selected from the top and high-quality journals based on the implications of MALL in higher education language learning....
The current study investigates the effects of Mobile assisted language learning (MALL) on collocations learning of Pakistani ESL students. The experimental study is an effort to understand if there is a significant difference in the use of collocations by ESL learners who learnt through two different methodologies, one through MALL and one without...
Recent studies found that expression of Nedd4-2 is reduced in lung tissue from patients with idiopathic pulmonary fibrosis (IPF) and that the conditional deletion of Nedd4-2 in lung epithelial cells causes IPF-like disease in adult mice via multiple defects, including dysregulation of the epithelial Na + channel (ENaC), TGFβ signaling and the biosy...
E-Commerce is a great platform for the customer to do shopping with a great amount of
comfort now a day. India is the largest customer market. As the importance of E-Commerce
business grows in the country, meanwhile there must be a need for flexibility of this online
platform to fulfil the expectations of customers. Merely growth of the internet a...
![Table 1 ]. 11](publication/350727704/figure/tbl1/AS:1024477009809410@1621265761308/11_Q320.jpg)
The causal and simulation theories are often presented as very distinct views about declarative memory, their major difference lying on the causal condition. The causal theory states that remembering involves an accurate representation causally connected to an earlier experience (the causal condition). In the simulation theory, remembering involves...
Citations
... Using CNN-LSTM and Word2Vec, it improves accuracy in Roman Urdu, addressing language processing gaps. Wang H et al [13] focuses on product review sentiment analysis. It combines ALBERT and LSTM models to improve performance, outperforming standalone models, offering insights for user preferences and product decision-making. ...
In this research work, an integrated sentiment analysis system is designed specifically for movie reviews. Leveraging LSTM-based sentiment analysis, Proposed implementation simplifies model training using pre-trained LSTM models, resulting in robust sentiment prediction capabilities. The architecture emphasizes abstraction, separating frontend and backend components, optimizing data flow with JSON formatting and Pickle Files, and providing a user-friendly interface. By highlighting the role of abstraction in enhancing user experiences and system efficiency, the research reveals the successful synergy of DL techniques, architectural design, and abstraction principles. The LSTM model achieved testing accuracy of 87.53%.
... The model is trained by the directional propagation algorithm, and the information matrix obtained after the bidirectional LSTM model processing is classified by a Softmax classifier [25]. The goal of the model's training phase is to minimise the loss function. ...
Retraction: [Tao Cheng, Lianjiang Li, Optimization of E‐commerce platform marketing method and comment recognition model based on deep learning and intelligent blockchain, IET Software 2023 (https://doi.org/10.1049/sfw2.12117)].
The above article from IET Software, published online on 3 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
The metaverse is a nascent concept that envisions a virtual universe, a collaborative space where individuals
can interact, create, and participate in a wide range of activities. Privacy in the metaverse is a critical concern
as the concept evolves and immersive virtual experiences become more prevalent. The metaverse privacy
problem refers to the challenges and concerns surrounding the privacy of personal information and data within
Virtual Reality (VR) environments as the concept of a shared VR space becomes more accessible. Metaverse
will harness advancements from various technologies such as Artificial Intelligence (AI), Extended Reality (XR)
and Mixed Reality (MR) to provide personalized and immersive services to its users. Moreover, to enable more
personalized experiences, the metaverse relies on the collection of fine-grained user data that leads to various
privacy issues. Therefore, before the potential of the metaverse can be fully realized, privacy concerns related
to personal information and data within VR environments must be addressed. This includes safeguarding users’
control over their data, ensuring the security of their personal information, and protecting in-world actions
and interactions from unauthorized sharing. In this paper, we explore various privacy challenges that future
metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences,
and facilitating interactions. Moreover, we thoroughly analyze technical solutions such as differential privacy,
Homomorphic Encryption, and Federated Learning and discuss related sociotechnical issues regarding privacy.
Sentiment Analysis tasks are made considerably simpler by extracting subjective statements from online reviews, thereby reducing the overhead of the classifiers. The review dataset encompasses both subjective and objective sentences, where subjective writing expresses the author's opinions, and objective text presents factual information. Assessing the subjectivity of review statements involves categorizing them as objective or subjective. The effectiveness of word vectors plays a crucial role in this process, as they capture the semantics and contextual cues of a subjective language. This study investigates the significance of employing sophisticated word vector representations to enhance the detection of subjective reviews. Several methodologies for generating word vectors have been investigated, encompassing both conventional approaches, such as Word2Vec and Global Vectors for word representation, and recent innovations, such as like Bidirectional Encoder Representations from Transformers (BERT), ALBERT, and Embeddings from Language Models. These neural word embeddings were applied using Keras and Scikit-Learn. The analysis focuses on Cornell subjectivity review data within the restaurant domain, and metrics evaluating performance, such as accuracy, F1-score, recall, and precision, are assessed on a dataset containing subjective reviews. A wide range of conventional vector models and deep learning-based word embeddings are utilized for subjective review classification, frequently in combination with deep learning architectures like Long Short-Term Memory (LSTM). Notably, pre-trained BERT-base word embeddings exhibited exceptional accuracy of 96.4%, surpassing the performance of all other models considered in this study. It has been observed that BERT-base is expensive because of its larger structure.
This research examines the impact of fake reviews on consumer satisfaction within the e-commerce domain, using a dataset from JD.com to develop three statistical models. The analysis reveals that although fake reviews initially boost product evaluations, consumers eventually recognize the falsehoods, leading to diminished satisfaction and trust in the e-commerce platform. The study highlights the critical importance of differentiating between genuine and counterfeit reviews, employing an LSTM model to enhance accuracy, and underscores the negative effects of fake reviews on consumer perceptions and the integrity of e-commerce. The findings suggest a need for stringent governance of online reviews to preserve a healthy digital marketplace, safeguard consumer interests, and ensure the authenticity of customer feedback. Offering valuable insights into addressing the challenges posed by fake reviews, this research emphasizes their adverse effects on customer satisfaction and trust in e-commerce.
