Convolutional Neural Network based Filtering-Scoring System for Rating Prediction of Travel Attractions using Social Media

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

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Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽

10.1155/2021/6694538 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Lingfeng Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Prediction Model ◽

Convolutional Neural Network ◽

Network Structure ◽

Rating Data ◽

Tv Program ◽

Neural Network Structure ◽

Rating Prediction ◽

Tv Show

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

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Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran

10.20944/preprints202102.0554.v1 ◽

2021 ◽

Author(s):

Mohammad Javad Shooshtari ◽

Hossein Etemadfard ◽

Rouzbeh Shad

Keyword(s):

Neural Network ◽

Neural Networks ◽

Social Media ◽

Convolutional Neural Network ◽

Business Owners ◽

Shopping Centers ◽

Multi Layer Perceptron ◽

Commercial Activities ◽

Enormous Amount

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

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Neural User Response Generator: Fake News Detection with Collective User Intelligence

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/533 ◽

2018 ◽

Cited By ~ 24

Author(s):

Feng Qian ◽

Chengyue Gong ◽

Karishma Sharma ◽

Yan Liu

Keyword(s):

Neural Network ◽

Social Media ◽

Early Detection ◽

Convolutional Neural Network ◽

Semantic Information ◽

Experimental Results ◽

News Article ◽

Fake News ◽

Additional Information ◽

Word Level

Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake. We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.

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Facial Expression Recognition Based Scoring System for Restaurants using Deep Learning

International Journal of Emerging Science and Engineering ◽

10.35940/ijese.h2467.036820 ◽

2020 ◽

Vol 6 (8) ◽

pp. 6-9

Keyword(s):

Neural Network ◽

Deep Learning ◽

Facial Expression ◽

Convolutional Neural Network ◽

Scoring System ◽

Facial Expression Recognition ◽

Direct Contact ◽

Web Server ◽

Rating System ◽

Expression Recognition

Now a days, the technology has been increased. Hence this is a tool for the advancement of the earlier things. So we are making use of the technology for restaurant scoring system. The fame of unmanned restaurants is the hot topic in society. Because of the absence of the staff, there is no direct contact with the customers to take feedback of the restaurant. This paper represents the automated rating system for the restaurants by detecting the facial emotions with the help of Convolutional Neural Network [CNN] .It consists of web server and a pretrained CNN and detection of facial expression.

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