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Enhanced Sentiment analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

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Intelligent Automation & Soft Computing 2023年第1期35卷 1015-1034页

作者： Ayman Mohamed Mostafa College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia

Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects.Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy.The Sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms.Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process.In unsupervised mechanisms,a lexicon is constructed for storing polarity terms.The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms.In addition,most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process.Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process.This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent Sentiment analysis research.A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms.An interactive Sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms.An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically.The experimental results are conducted on two datasets to measure the over-all accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

关键词： Sentiment analysis semi-supervised framework multi-weight polarity algorithm Arabic lexicons and automated scaling algorithm

A Parallel Approach for Sentiment analysis on Social Networks Using Spark

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Intelligent Automation & Soft Computing 2023年第2期35卷 1831-1842页

作者： M.Mohamed Iqbal K.Latha Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai600062India University College of Engineering Anna University(B.I.T Campus)Tiruchirappalli620024India

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefﬁcient for Sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ﬁcult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for Sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classiﬁed using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classiﬁer in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-ﬁer trained with this new dataset was more than 80%.

关键词： Social networks Sentiment analysis big data spark tweets classiﬁcation

Dragonfly Optimization with Deep Learning Enabled Sentiment analysis for Arabic Tweets

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Computer Systems Science & Engineering 2023年第8期46卷 2555-2570页

作者： Aisha M.Mashraqi Hanan T.Halawani College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Sentiment analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social media.In literature,it has been reported that SA data is created for English language in excess of any other language.It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language.An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.Neural word embedding has been employed in literature,since it is less labor-intensive than automatic feature engineering.By ignoring the context of sentiment,most of the word-embedding models follow syntactic data of words.The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment analysis for Arabic Tweets(DFODLSAAT)model.The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language.At first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful format.In addition,TF-IDF model is exploited as a feature extractor to generate the feature vectors.Besides,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of sentiments.At last,the hyperparameters of ABLSTM model are optimized using DFO algorithm.The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects.The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.

关键词： Natural language processing Sentiment analysis arabic tweets deep learning metaheuristics lexicon approach

Predicting Bitcoin Trends Through Machine Learning Using Sentiment analysis with Technical Indicators

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Computer Systems Science & Engineering 2023年第8期46卷 2231-2246页

作者： Hae Sun Jung Seon Hong Lee Haein Lee Jang Hyun Kim Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea Department of Interaction Science/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

关键词： Bitcoin cryptocurrency Sentiment analysis price trends prediction natural language processing machine learning

Sentiment analysis with Tweets Behaviour in Twitter Streaming API

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Computer Systems Science & Engineering 2023年第5期45卷 1113-1128页

作者： Kuldeep Chouhan Mukesh Yadav Ranjeet Kumar Rout Kshira Sagar Sahoo NZ Jhanjhi Mehedi Masud Sultan Aljahdali Computer Science and Engineering I.T.S Engineering CollegeGreater Noida201310India Computer Science and Engineering DPG Institute of Technology and ManagementGurgaon122004India Computer Science and Engineering National Institute of Technology SrinagarJammu and KashmirIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAndhra Pradesh522240India School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter Sentiment analysis to recognize the stakeholder group.The Sentiment analysis needs an advanced level of approaches including adoption to encompass data Sentiment analysis and various machine learning tools.An assessment of Sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for Sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

关键词： Machine learning Naive Bayes natural language processing Sentiment analysis social media analytics support vector machine Twitter application programming interface

Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment analysis

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Computer Systems Science & Engineering 2023年第10期47卷 1233-1247页

作者： Abeer D.Algarni Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches.Several natural language processing(NLP)tools are commonly used to examine the large quantity of data generated online.Particularly,Sentiment analysis(SA)is an effective way of classifying the data into different classes of user opinions or sentiments.The latest advances in machine learning(ML)and deep learning(DL)approaches offer an intelligent way of analyzing sentiments.In this view,this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for Sentiment analysis(WIESFO-DLSA)technique.The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data.The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features.At the same time,bidirectional long short term memory(BiLSTM)model is applied for classification of sentiments into different class labels.Moreover,an enhanced sunflower optimization(ESFO)algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model.A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures.

关键词： Sentiment analysis web intelligence deep learning social networking natural language processing

Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment analysis

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Computer Systems Science & Engineering 2023年第10期47卷 1249-1266页

作者： Abdelwahed Motwakel Najm Alotaibi Eatedal Alabdulkreem Hussain Alshahrani MohamedAhmed Elfaki Mohamed K Nour Radwa Marzouk Mahmoud Othman Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Sentiment analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.

关键词： Corpus linguistics Sentiment analysis natural language processing deep learning word embedding

Sentiment analysis using deep learning approaches:an overview

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Science China(Information Sciences) 2020年第1期63卷 21-56页

作者： Olivier HABIMANA Yuhua LI Ruixuan LI Xiwu GU Ge YU School of Computer Science and Technology Huazhong University of Science and Technology School of Computer Science and Engineering Northeastern University

Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of data in an enormous and dynamic way. In this regard, the Sentiment analysis appeared to be an important tool that allows the automation of getting insight from the user-generated data. Recently, deep learning approaches have been proposed for different Sentiment analysis tasks and have achieved state-of-the-art results. Therefore, in order to help researchers to depict quickly the current progress as well as current issues to be addressed, in this paper, we review deep learning approaches that have been applied to various Sentiment analysis tasks and their trends of development. This study also provides the performance analysis of different deep learning models on a particular dataset at the end of each Sentiment analysis task. Toward the end, the review highlights current issues and hypothesized solutions to be taken into account in future work. Moreover, based on knowledge learned from previous studies, the future work subsection shows the suggestions that can be incorporated into new deep learning models to yield better performance. Suggestions include the use of bidirectional encoder representations from transformers(BERT), sentiment-specific word embedding models, cognition-based attention models, common sense knowledge, reinforcement learning, and generative adversarial networks.

关键词： Sentiment analysis opinion mining deep learning neural network natural language processing(NLP) social network

Structural information aware deep semi-supervised recurrent neural network for Sentiment analysis

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Frontiers of Computer Science 2015年第2期9卷 171-184页

作者： Wenge RONG Baolin PENG Yuanxin OUYANG Chao LI Zhang XIONG School of Computer Science and Engineering Beihang University Beijing 100191 China Research Institute of Beihang University in Shenzhen Shenzhen 518057 China

With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the Sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel Sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.

关键词： Sentiment analysis recurrent neural network,deep learning machine learning