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  • Open Access


    Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence Using Joint Sentiment Topic Detection with MEDLDA

    PCD Kalaivaani1,*, Dr. R Thangarajan2

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 71-79, 2020, DOI:10.31209/2019.100000152

    Abstract Web mining is the process of integrating the information from web by traditional data mining methodologies and techniques. Opinion mining is an application of natural language processing to extract subjective information from web. Online reviews require efficient classification algorithms for analysing the sentiments, which does not perform an in–depth analysis in current methods. Sentiment classification is done at document level in combination with topics and sentiments. It is based on weakly supervised Joint Sentiment-Topic mode which extends the topic model Maximum Entropy Discrimination Latent Dirichlet Allocation by constructing an additional sentiment layer. It is assumed that topics generated are dependent… More >

  • Open Access


    Sentiment Analysis Using Deep Learning Approach

    Peng Cen1, Kexin Zhang1, Desheng Zheng1, *

    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 >

  • Open Access


    Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

    Ao Feng1, Zhengjie Gao1, *, 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 >

  • Open Access


    Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

    Yun Wang1, Fazli Subhan2, Shahaboddin Shamshirband3, 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 >

  • Open Access


    User Profile System Based on Sentiment Analysis for Mobile Edge Computing

    Sang-Min Park1, Young-Gab Kim2, *

    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 >

  • Open Access


    Sentiment Analysis Method Based on Kmeans and Online Transfer Learning

    Shengting Wu1, Yuling Liu1,*, 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 >

  • Open Access


    Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

    Feng Xu1, Xuefen Zhang2,*, 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 >

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