Considerations about learning Word2Vec

Gennaro, Giovanni Di; Buonanno, Amedeo; Palmieri, F.

doi:10.1007/s11227-021-03743-2

Cited by 55 publications

(21 citation statements)

References 25 publications

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“…Word -Word2vec Word2vec [36] is a word embedding approach that can be used to represent words as a vector. The process of creating a word dictionary was carried out utilizing a set of articles from the Python sklearn module's natural language tool kit.…”

Section: Process Resultsmentioning

confidence: 99%

Sentiment Analysis on E-Commerce Apparels using Convolutional Neural Network

Mehta¹,

Panda²

2022

IJC

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The Fourth Industrial Revolution (4.0) is a fusion of advances in Artificial Intelligence (AI), Robotics, the Internet of Things (IoT), Genetic Engineering, Quantum Computing, and other technologies. A large number of people are using internet-based services as a result of enhanced internet infrastructure and decreased costs. As a result, such businesses' attempts to penetrate internet media are disrupted. The e-commerce company, like Amazon, offers both customer-to-customer and business-to-business services in the apparel sector. Companies must understand the needs of buyers to maximize their profits. As a result, consumer sentiment analysis is carried out. However, because this procedure is time-consuming, it is made automatically utilizing artificial intelligence approaches. According to the findings of a study on sentiment analysis on an E-Commerce-based web store for women, the apparels review dataset using the CNN method with the word vector generator and TF-IDF can produce a higher accuracy of 94%.

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Section: Process Resultsmentioning

confidence: 99%

Sentiment Analysis on E-Commerce Apparels using Convolutional Neural Network

Mehta¹,

Panda²

2022

IJC

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“…Word2Vec is a popular NLP technique based on neural network to learn word embeddings from 100 billion words [11][12][13]. Google's Word2vec model has the ability to detect synonyms of words or suggest suitable words for an incomplete sentence.…”

Section: Word2vecmentioning

confidence: 99%

Insider Threat Detection Based on NLP Word Embedding and Machine Learning

Haq¹,

Khan²,

Alshehri³

2022

Intelligent Automation &Amp; Soft Computing

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The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires stateof-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which are major concerns needing further investigation. In this paper, an attempt to fulfill these gaps by detecting insider threats with the novelties of the present investigation first developed two deep learning hybrid LSTM models integrated with Google's Word2vec LSTM (Long Short-Term Memory) GLoVe (Global Vectors for Word Representation) LSTM. Secondly, the performance of two hybrid DL models was compared with the state-of-the-art ML models such as XGBoost, Ada-Boost, RF (Random Forest), KNN (K-Nearest Neighbor) and LR (Logistics Regression). Thirdly, the present investigation bridges the gaps of using a real dataset, high accuracy, and significantly lower false alarm rate. It was found that ML-based models outperformed the DL-based ones. The results were evaluated based on earlier studies and deemed efficient at detecting insider threats using the real dataset.

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“…On Ctrip datasets, TF-IDF, LSI, Word2Vec [ 31 ], and Doc2vec [ 32 ] represent using these methods to extract data features and using Euclidean distance to calculate sample similarity. Jaccard represents using the Jaccard computing method to measure the similarity between text samples.…”

Section: Experiments and Analysismentioning

confidence: 99%

Multimodal Feature Fusion Based Hypergraph Learning Model

Yang

Zhao

2022

Computational Intelligence and Neuroscience

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Hypergraph learning is a new research hotspot in the machine learning field. The performance of the hypergraph learning model depends on the quality of the hypergraph structure built by different feature extraction methods as well as its incidence matrix. However, the existing models are all hypergraph structures built based on one feature extraction method, with limited feature extraction and abstract expression ability. This paper proposed a multimodal feature fusion method, which firstly built a single modal hypergraph structure based on different feature extraction methods, and then extended the hypergraph incidence matrix and weight matrix of different modals. The extended matrices fuse the multimodal abstract feature and an expanded Markov random walk range during model learning, with stronger feature expression ability. However, the extended multimodal incidence matrix has a high scale and high computational cost. Therefore, the Laplacian matrix fusion method was proposed, which performed Laplacian matrix transformation on the incidence matrix and weight matrix of every model, respectively, and then conducted a weighted superposition on these Laplacian matrices for subsequent model training. The tests on four different types of datasets indicate that the hypergraph learning model obtained after multimodal feature fusion has a better classification performance than the single modal model. After Laplace matrix fusion, the average time can be reduced by about 40% compared with the extended incidence matrix, the classification performance can be further improved, and the index F1 can be improved by 8.4%.

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Considerations about learning Word2Vec

Cited by 55 publications

References 25 publications

Sentiment Analysis on E-Commerce Apparels using Convolutional Neural Network

Sentiment Analysis on E-Commerce Apparels using Convolutional Neural Network

Insider Threat Detection Based on NLP Word Embedding and Machine Learning

Multimodal Feature Fusion Based Hypergraph Learning Model

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