Peng Cen¹, Kexin Zhang¹, Desheng Zheng^{1, *}

Journal on Artificial Intelligence, Vol.2, No.1, pp. 17-27, 2020, DOI:10.32604/jai.2020.010132

Abstract Deep learning has made a great breakthrough in the field of speech and image recognition. Mature deep learning neural network has completely changed the field of nat ural language processing (NLP). Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the Internet and other media, sentiment analysis has become one of the most active research fields in natural language processing. This paper introduces three deep learning networks applied in IMDB movie reviews sent iment analysis. Dataset was divided to 50% positive reviews and 50% negative reviews. Recurrent Neural Network (RNN) and Long Short-Term… More >

Yixian Zhang¹, Jieren Cheng^{2, *}, Yifan Yang², Haocheng Li², Xinyi Zheng², Xi Chen², Boyi Liu³, Tenglong Ren⁴, Naixue Xiong⁵

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1415-1434, 2020, DOI:10.32604/cmc.2020.011316

Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the… More >

Ao Feng¹, Zhengjie Gao^{1, *}, Xinyu Song¹, Ke Ke², Tianhao Xu¹, Xuelei Zhang¹

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 909-923, 2020, DOI:10.32604/cmc.2020.09913

Abstract Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional… More >

Yun Wang¹, Fazli Subhan², Shahaboddin Shamshirband^{3, 4, *}, Muhammad Zubair Asghar⁵, Ikram Ullah⁵, Ammara Habib⁵

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 631-655, 2020, DOI:10.32604/cmc.2020.07920

Abstract The feedback collection and analysis has remained an important subject matter for long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction… More >

Sang-Min Park¹, Young-Gab Kim^{2, *}

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 569-590, 2020, DOI:10.32604/cmc.2020.08666

Abstract Emotions of users do not converge in a single application but are scattered across diverse applications. Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications. In this paper, we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity, media diversity, and server-side solution. Fine-grained data requires additional data and structural analysis in mobile devices. Media diversity requires standard parameters to integrate user data from various applications. A server-side… More >

Kemal Akyol^1,*, Baha Şen²

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 69-80, 2019, DOI:10.32604/cmc.2019.08143

Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets… More >

Shengting Wu¹, Yuling Liu^1,*, Jingwen Wang², Qi Li¹

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1207-1222, 2019, DOI:10.32604/cmc.2019.05835

Abstract Sentiment analysis is a research hot spot in the field of natural language processing and content security. Traditional methods are often difficult to handle the problems of large difference in sample distribution and the data in the target domain is transmitted in a streaming fashion. This paper proposes a sentiment analysis method based on Kmeans and online transfer learning in the view of fact that most existing sentiment analysis methods are based on transfer learning and offline transfer learning. We first use the Kmeans clustering algorithm to process data from one or multiple source domains and select the data similar… More >

Xiaodong Yan^1,2, Wei Song^1,2,*, Xiaobing Zhao^1,2, Anti Wang³

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 707-719, 2019, DOI:10.32604/cmc.2019.05157

Abstract We apply the semi-supervised recursive autoencoders (RAE) model for the sentiment classification task of Tibetan short text, and we obtain a better classification effect. The input of the semi-supervised RAE model is the word vector. We crawled a large amount of Tibetan text from the Internet, got Tibetan word vectors by using Word2vec, and verified its validity through simple experiments. The values of parameter α and word vector dimension are important to the model effect. The experiment results indicate that when α is 0.3 and the word vector dimension is 60, the model works best. Our experiment also shows the… More >

Feng Xu¹, Xuefen Zhang^2,*, Zhanhong Xin¹, Alan Yang³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 697-709, 2019, DOI:10.32604/cmc.2019.05375

Abstract Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far… More >

Yuhong Zhang^1,*, Qinqin Wang¹, Yuling Li¹, Xindong Wu²

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 285-297, 2018, DOI: 10.3970/cmc.2018.02604

Abstract Recently, the effectiveness of neural networks, especially convolutional neural networks, has been validated in the field of natural language processing, in which, sentiment classification for online reviews is an important and challenging task. Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment classification. Firstly, with a sentence presented by word vectors, convolution operation is introduced to obtain the convolution feature map vectors. Secondly, these vectors are segmented according… More >