Ameen Banjar¹, Zohair Ahmed², Ali Daud¹, Rabeeh Ayaz Abbasi³, Hussain Dawood^4,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2203-2225, 2021, DOI:10.32604/cmc.2021.014226

Abstract Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if… More >

Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7, Zulkefli Mansor⁸, Kim-Hung Pho⁹

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1085-1101, 2021, DOI:10.32604/cmc.2021.013836

Abstract Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation… More >

Belal Abdullah Hezam Murshed^1,∗, Hasib Daowd Esmail Al-ariki^2,†, Suresha Mallappa^3,‡

Computer Systems Science and Engineering, Vol.35, No.6, pp. 495-512, 2020, DOI:10.32604/csse.2020.35.495

Abstract This paper conducts a comprehensive review of various word and sentence semantic similarity techniques proposed in the literature. Corpus-based, Knowledge-based, and Feature-based are categorized under word semantic similarity techniques. String and set-based, Word Order-based Similarity, POSbased, Syntactic dependency-based are categorized as sentence semantic similarity techniques. Using these techniques, we propose a model for computing the overall accuracy of the twitter dataset. The proposed model has been tested on the following four measures: Atish’s measure, Li’s measure, Mihalcea’s measure with path similarity, and Mihalcea’s measure with Wu and Palmer’s (WuP) similarity. Finally, we evaluate the proposed method on three real-world twitter… More >

Enlu Li¹, Zhangjie Fu^1,2,3,*, Siyu Chen¹, Junfu Chen¹

Journal of Cyber Security, Vol.2, No.4, pp. 183-190, 2020, DOI:10.32604/jcs.2020.015010

Abstract With the development of natural language processing, deep learning, and other technologies, text steganography is rapidly developing. However, adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis. If terrorists use the text steganography method to spread terrorist messages, it will greatly disturb social stability. Steganalysis methods, especially those for resisting adversarial attacks, need to be further improved. In this paper, we propose a two-stage highly robust model for text steganalysis. The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels. In the first phase, every sentence is first transformed into… More >

Zia Ul Rehman^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Ghulam Mustafa², Hira Fayyaz⁴, Muhammad Hanif^1,2, Muhammad Anwar Saeed⁵

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1075-1090, 2021, DOI:10.32604/cmc.2020.012770

Abstract The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new… More >

Can Eyupoglu

Computer Systems Science and Engineering, Vol.34, No.3, pp. 113-121, 2019, DOI:10.32604/csse.2019.34.113

Abstract In recent years, the topic of Natural Language Processing (NLP) has attracted increasing interest. Many NLP applications including machine translation, machine learning, speech recognition, sentiment analysis, semantic search and natural language generation have been developed for most of the existing languages. Besides, two-level morphological description of the language to be used is required for these applications. However, there is no comprehensive study of Bashkir Turkish in the literature. In this paper, a two-level description of Bashkir Turkish morphology is described. The description based on a root word lexicon of Bashkir Turkish is implemented using Extensible Markup Language (XML) and appended… More >

Xu Zhang¹, Wenpeng Lu^1,*, Fangfang Li^2,3, Xueping Peng³, Ruoyu Zhang¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 601-616, 2019, DOI:10.32604/cmc.2019.06045

Abstract Sentence semantic matching (SSM) is a fundamental research in solving natural language processing tasks such as question answering and machine translation. The latest SSM research benefits from deep learning techniques by incorporating attention mechanism to semantically match given sentences. However, how to fully capture the semantic context without losing significant features for sentence encoding is still a challenge. To address this challenge, we propose a deep feature fusion model and integrate it into the most popular deep learning architecture for sentence matching task. The integrated architecture mainly consists of embedding layer, deep feature fusion layer, matching layer and prediction layer.… More >