scholarly journals SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Dyah Apriliani ◽  
Taufiq Abidin ◽  
Edhy Sutanta ◽  
Amir Hamzah ◽  
Oman Somantri
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Software Validation ◽  
Application Development ◽  
Validation Test ◽  
Proposed Model ◽  
Hotel Service ◽  
Analysis Application ◽  
Evaluation Stage ◽  
The Right

An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.

Sarcasm Detection Using Multi-Channel Attention Based BLSTM On News Headline

2020 ◽  
Author(s):  
Azika Syahputra Azwar ◽  
Suharjito
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Important Task ◽  
Hybrid Neural Network ◽  
Negative Opinion ◽  
Proposed Model ◽  
Short Memory ◽  
Literary Devices

Abstract Sarcasm is often used to express a negative opinion using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, an important task of sentiment analysis. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we introduce a multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) network to detect sarcastic headline on the news. Multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) proposed model was evaluated on the news headline dataset, and the results-compared to the CNN-LSTM and Hybrid Neural Network were excellent.

scholarly journals Marketplace affiliates potential analysis using cosine similarity and vision-based page segmentation

2020 ◽  
Vol 9 (6) ◽  
pp. 2492-2498
Author(s):  
Wildan Budiawan Zulfikar ◽  
Mohamad Irfan ◽  
Muhammad Ghufron ◽  
Jumadi Jumadi ◽  
Esa Firmansyah
Keyword(s):  
Positive Impact ◽  
Objective Assessment ◽  
Cosine Similarity ◽  
Success Factor ◽  
Page Segmentation ◽  
Proposed Model ◽  
Measured System ◽  
Evaluation Stage ◽  
The Right

One success factor of an online affiliate is determined by the quality of the content source. Therefore, affiliate marketplaces need to do an objective assessment to retrieve content data that will be used to choose the right product in the appropriate product filter. Usually, the selection is not made using a good and measured system so that the selection of product content is only based on parts that are not in accordance with what is seen or subjective. However, if analyzed using a good and measurable system will produce an objective product content and can have a positive impact on users because the selection is based on factual data. The purpose of this research is to analyze the potential of the affiliate marketplace by combining cosine similarity with vision-based page segmentation. This is a new breakthrough made for optimization to get the best content in accordance with the required criteria. This work will produce a number of product recommendations that are appropriate for publication and then made use of for comparison that matches the required criteria. At the limited evaluation stage, the performance of the proposed model obtained satisfactory results, in which 5 queries tested were all as expected. 

scholarly journals A Grading System-based Model for Detecting Unbalanced Bids during the Tendering Process

2019 ◽  
Vol 50 (2) ◽  
pp. 139-147 ◽  
Author(s):  
Gul Polat ◽  
Harun Turkoglu ◽  
Atilla Damci
Keyword(s):  
Evaluation Process ◽  
Unit Price ◽  
Fair Competition ◽  
Lump Sum ◽  
Proposed Model ◽  
Grading Systems ◽  
Evaluation Stage ◽  
Bid Evaluation ◽  
The Individual ◽  
The Right

Unbalanced bidding is a common practice used in both unit price and lump sum contracts. Contractors may unbalance their bids in different forms for various reasons. The studies in the literature either focus on developing optimization models that assist contractors in winning contracts and maximizing profits of their bids through unbalancing or developing models that assist owners in detecting and/or preventing unbalanced bids during the bid evaluation stage. Unbalanced bidding is one of the most controversial subjects in the construction management literature and practice. Although there is no consensus on whether it is unethical or not, this practice is not usually for the benefit of owners. Therefore, owners have the right to reject the unbalanced bids and create a fair competition environment if they have a mechanism to detect it during the bid evaluation process. The main objective of this study is to propose a model, which consists of five different grading systems and helps owners in detecting unbalanced bids during the tendering process. In the proposed model, owners may either calculate the individual grades of each bidder or calculate the final score of each bidder by assigning different weights to these grading systems according to the project characteristics or their own needs. The final scores and bid prices of the contractors can be simultaneously evaluated. In order to demonstrate the applicability of the proposed model, an illustrative example is presented. It can be concluded that the proposed model can be effectively and easily used by owners for detecting unbalanced bids. This paper is the revised version of the paper that has been published in the proceedings of the Creative Construction Conference 2018 (Polat et al., 2018).

scholarly journals Stance Classification with Target-specific Neural Attention

2017 ◽  
Author(s):  
Jiachen Du ◽  
Ruifeng Xu ◽  
Yulan He ◽  
Lin Gui
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Specific Information ◽  
Target Information ◽  
Specific Target ◽  
Proposed Model ◽  
Text Content

Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-the-art performance.

Syntax-ignorant N-gram embeddings for dialectal Arabic sentiment analysis

2020 ◽  
pp. 1-24
Author(s):  
Hala Mulki ◽  
Hatem Haddad ◽  
Mourad Gridach ◽  
Ismail Babaoğlu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Syntactic Structure ◽  
Network Architectures ◽  
Proposed Model ◽  
Arabic Dialects ◽  
Neural Network Architectures ◽  
N Gram ◽  
Arabic Sentiment Analysis

Abstract Arabic sentiment analysis models have recently employed compositional paragraph or sentence embedding features to represent the informal Arabic dialectal content. These embeddings are mostly composed via ordered, syntax-aware composition functions and learned within deep neural network architectures. With the differences in the syntactic structure and words’ order among the Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant, sentiment-specific n-gram embeddings for sentiment analysis of several Arabic dialects. The novelty of the proposed model is illustrated through its features and architecture. In the proposed model, the sentiment is expressed by embeddings, composed via the unordered additive composition function and learned within a shallow neural architecture. To evaluate the generated embeddings, they were compared with the state-of-the art word/paragraph embeddings. This involved investigating their efficiency, as expressive sentiment features, based on the visualisation maps constructed for our n-gram embeddings and word2vec/doc2vec. In addition, using several Eastern/Western Arabic datasets of single-dialect and multi-dialectal contents, the ability of our embeddings to recognise the sentiment was investigated against word/paragraph embeddings-based models. This comparison was performed within both shallow and deep neural network architectures and with two unordered composition functions employed. The results revealed that the introduced syntax-ignorant embeddings could represent single and combinations of different dialects efficiently, as our shallow sentiment analysis model, trained with the proposed n-gram embeddings, could outperform the word2vec/doc2vec models and rival deep neural architectures consuming, remarkably, less training time.

scholarly journals Prediction Outcome for Massive Multiplayer Online Games Using Data Mining

2018 ◽  
Vol 11 (1) ◽  
pp. 248 ◽  
Author(s):  
Shazwani Samsurim ◽  
Nor Ashikin Mohamad Kamal ◽  
Marina Ismail ◽  
Norizan Mat Diah
Keyword(s):  
Cross Validation ◽  
Online Games ◽  
Classification Algorithms ◽  
Multiplayer Online Games ◽  
Validation Test ◽  
Prediction Outcome ◽  
Proposed Model ◽  
Using Data ◽  
The Right ◽  
Fold Cross Validation

Massive Multiplayer Online (MMO) game is one of the famous game genres among teenagers nowadays. MMO games allow gamers to interact and play with up to thousand players. Rainbow Six Siege (RSS) belongs to MMO type of game. However, due to many operators that are available in this game, the player needs to choose the right operator to counter the enemy operator. Therefore, based on the characteristic of the selected operator, this paper attempted to predict the outcomes of the game.  In our prediction model, characteristics for these operators were extracted from 120 live stream replays. Three classification algorithms were utilized to predict the outcome of the game. Among these algorithms, IBK had obtained outstanding performance in the dataset. The accuracy of the model is 93.75%, applying 5-fold cross-validation test. The success rate reveals that our proposed model is suitable to predict the outcome of the game.

A Tracer Study of ICT Graduate Students at Mzuzu University, Malawi

Mousaion ◽  
2019 ◽  
Vol 36 (3) ◽  
Author(s):  
Chimango Nyasulu ◽  
Winner Chawinga ◽  
George Chipeta
Keyword(s):  
Language Processing ◽  
Mobile Application ◽  
First Century ◽  
Application Development ◽  
Job Requirements ◽  
Level Of Satisfaction ◽  
Information And Communication ◽  
Quantitative Design ◽  
The Right ◽  
The University

Governments the world over are increasingly challenging universities to produce human resources with the right skills sets and knowledge required to drive their economies in this twenty-first century. It therefore becomes important for universities to produce graduates that bring tangible and meaningful contributions to the economies. Graduate tracer studies are hailed to be one of the ways in which universities can respond and reposition themselves to the actual needs of the industry. It is against this background that this study was conducted to establish the relevance of the Department of Information and Communication Technology at Mzuzu University to the Malawian economy by systematically investigating occupations of its former students after graduating from the University. The study adopted a quantitative design by distributing an online-based questionnaire with predominantly closed-ended questions. The study focused on three key objectives: to identify key employing sectors of ICT graduates, to gauge the relevance of the ICT programme to its former students’ jobs and businesses, and to establish the level of satisfaction of the ICT curriculum from the perspectives of former ICT graduates. The key findings from the study are that the ICT programme is relevant to the industry. However, some respondents were of the view that the curriculum should be strengthened by revising it through an addition of courses such as Mobile Application Development, Machine Learning, Natural Language Processing, Data Mining, and LINUX Administration to keep abreast with the ever-changing ICT trends and job requirements. The study strongly recommends the need for regular reviews of the curriculum so that it is continually responding to and matches the needs of the industry.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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