scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

scholarly journals Malicious URL Detection Algorithm Based on Multi Neural Network Series

CONVERTER ◽  
2021 ◽  
pp. 579-590
Author(s):  
Weirong Xiu
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Global Features ◽  
Multi Neural Network

Convolutional neural network based on attention mechanism and a bidirectional independent recurrent neural network tandem joint algorithm (CATIR) are proposed. In natural language processing related technologies, word vector features are extracted based on URLs, and the extracted URL information features and host information features are merged. The proposed CATIR algorithm uses CNN (Convolutional Neural Network) to obtain the deep local features in the data, uses the Attention mechanism to adjust the weights, and uses IndRNN (Independent Recurrent Neural Network) to obtain the global features in the data. The experimental results shows that the CATIR algorithm has significantly improved the accuracy of malicious URL detection based on traditional algorithms to 96.9%.

scholarly journals Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

2020 ◽  
Vol 10 (17) ◽  
pp. 5841 ◽  
Author(s):  
Beakcheol Jang ◽  
Myeonghwi Kim ◽  
Gaspard Harerimana ◽  
Sang-ug Kang ◽  
Jong Wook Kim
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Short Term Memory ◽  
User Behavior ◽  
Hybrid Approach ◽  
Attention Mechanism

There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models.

Similarity of Sentences With Contradiction Using Semantic Similarity Measures

2020 ◽  
Author(s):  
M Krishna Siva Prasad ◽  
Poonam Sharma
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Word Order ◽  
Similarity Measures ◽  
Semantic Features ◽  
Short Text ◽  
Sentence Similarity ◽  
Expert Ratings

Abstract Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents an algorithm to handle negation. In sentence similarity, word pair similarity plays a significant role. Hence, this paper also discusses the similarity between word pairs. Existing semantic similarity measures do not handle antonyms accurately. Hence, this paper proposes an algorithm to handle antonyms. This paper also presents an antonym dataset with 111-word pairs and corresponding expert ratings. The existing semantic similarity measures are tested on the dataset. The results of the correlation proved that the expert ratings are in order with the correlation obtained from the semantic similarity measures. The sentence similarity is handled by proposing two algorithms. The first algorithm deals with the typical sentences, and the second algorithm deals with contradiction in the sentences. SICK dataset, which has sentences with negation, is considered for handling the sentence similarity. The algorithm helped in improving the results of sentence similarity.

Speech-Act Classification Using Convolutional Neural Network and Word Embedding

2018 ◽  
Vol 27 (06) ◽  
pp. 1850026
Author(s):  
Kyoungman Bae ◽  
Youngjoong Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Speech Acts ◽  
Speech Act ◽  
Distributed Representation ◽  
Learning Techniques

The application of deep learning techniques in natural language processing tasks has been increased in recent years. Many studies have used the deep learning techniques to obtain a distributed representation of features. In particular, the convolutional neural network (CNN) with the distributed representation have subsequently been shown to be effective for the natural language processing tasks. This paper presents how to apply the CNN to speech-act classification. Then we analyze the experimental results on two issues, how to solve two problems about sparse speech-acts in train data and out of vocabulary, and how to utilize the advantages of CNN in the speech-act classification. As a result, we obtain the significant improved performances when CNN is applied to the speech-act classification.

scholarly journals Food Reviews Classification using multi-label convolutional neural network text classifier

2021 ◽  
Vol 40 ◽  
pp. 01009
Author(s):  
Krutuja S Lasne ◽  
Sejal S Nandrekar ◽  
Ashraf A Khan ◽  
Tushar Ghorpade
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Processing Technique ◽  
Pipeline System ◽  
Goods And Services ◽  
Natural Language Processing Technique

Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.

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