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Aspect Categorization Using Domain-Trained Word Embedding and Topic Modelling
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Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.

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Publication Date
Fri Nov 21 2025
Journal Name
Journal Of Advances In Information Technology
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

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Publication Date
Fri Jan 01 2016
Journal Name
International Journal Of Advanced Computer Science And Applications
Automatic Approach for Word Sense Disambiguation Using Genetic Algorithms
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Abstract: Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a col

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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Physics: Conference Series
Improve topic modeling algorithms based on Twitter hashtags
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Abstract<p>Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned</p> ... Show More
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Simplified Novel Approach for Accurate Employee Churn Categorization using MCDM, De-Pareto Principle Approach, and Machine Learning
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Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

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Publication Date
Sun Jan 10 2016
Journal Name
British Journal Of Applied Science &amp; Technology
Robust Watermarking for Video Using Modulation Technique on RGB Domain
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In this paper, a robust invisible watermarking system for digital video encoded by MPEG-4 is presented. The proposed scheme provides watermark hidden by embedding a secret message (watermark) in the sprite area allocated in reference frame (I-frame). The proposed system consists of two main units: (i) Embedding unit and (ii) Extraction unit. In the embedding unit, the system allocates the sprite blocks using motion compensation information. The allocated sprite area in each I–frame is used as hosting area for embedding watermark data. In the extraction unit, the system extracts the watermark data in order to check authentication and ownership of the video. The watermark data embedding method is Blocks average modulation applied on RGB dom

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Simulation and Modelling of Electricity Usage Control and Monitoring System using ThingSpeak
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Renewable energy technology is growing fast especially photovoltaic (PV) system to move the conventional electricity generation and distribution towards smart grid. However, similar to monthly electricity bill, the PV energy producers can only monitor their energy PV generation once a month. Any malfuntion in PV system components may reduce the performance of the system without notice. Thus, developing a real-time monitoring system of PV production is very crucial for early detection. In addition, electricity consumption is also important to be monitored more frequently to increase energy savings awareness among consumers. Hardware based Internet-of-Thing (IoT) monitoring and control system is widely used. However, the implementation of

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Publication Date
Sat Sep 30 2023
Journal Name
Journal Of The College Of Education For Women
Topic Management in Selected Dementia Patients' Speeches: A Clinical Discourse Analysis Study
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Topic management is the awareness of how speakers deal with initiating, developing, changing, and ending a topic and how they fix the relationship when a misunderstanding occurs. It is such an important unit of conversation as it includes the transition from one strategy to the other to be accomplished in a systematic and orderly manner. These strategies are impaired in dementia patients thus lead to communication breakdown. This study aims at detecting the dementia patients' topic management strategies in selected speeches and answering the questions of which of these strategies are fully or partially detected in these speeches. The researchers use a qualitative method to examine the speeches of those patients and they adopt an eclectic

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Publication Date
Tue Dec 22 1998
Journal Name
College Of Education Ibn Rushd
Investigating Iraqi College students&#39; production of English Intra Word and Inter Word Consonant Clusters
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MR Younus, 1998

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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
Nondestructive Evaluation of Coatings Delamination Using Microwave Time Domain Reflectometry Technique
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Publication Date
Sun Jun 29 2025
Journal Name
Al-academy
The Narrative Treatment of The Climate Change Topic in Film
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لم تولد الجماهيرية العالية للسينما من فراغ، إنّما تحققت من إيمان المتلقي العميق، بأن الفيلم أبعد من محاولة للترفيه، فالسينما هي فلسفة العصر، التي تتناول القضايا المهمة لتقدمها عبر حكايتها مؤطّرة بمنطق محدّد ومدفوعة ببناء عاطفي مؤثر، يهيئ المتلقي للاهتمام بهذه القضايا واستيعابها، والتغير المناخي واحد من أهم المواضيع التي بدأت السينما في الحقبة السابقة بتبنيها وتقديمها ضمن بنى حكائية تزيد من وعي المتل

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