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Improving Sentiment analysis accuracy with emoji embedding

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Journal of Safety Science and Resilience 2021年第4期2卷 246-252页

作者： Chuchu Liu Fan Fang Xu Lin Tie Cai Xu Tan Jianguo Liu Xin Lu College of Systems Engineering National University of Defense TechnologyChangsha410073China College of Computer National University of Defense TechnologyChangsha410073China School of Software Engineering Shenzhen Institute of Information TechnologyShenzhen518172China Institute of Accounting and Finance Shanghai University of Finance and EconomicsShanghai200433China

Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new source of individual sentiment,emojis,which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations.We examined popular Sentiment analysis algorithms,including rule-based and classification algorithms,to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance.Emojis were also translated into corresponding sentiment words when con-structing features for comparison with those directly generated from emoji label words.In addition,considering different functions of emojis in texts,we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance.We found that emojis are effective as expanding features for improving the accuracy of Sentiment analysis algorithms,and the algorithm performance can be further increased by taking different emoji usages into consideration.In this study,we developed an improved emoji-embedding model based on Bi-LSTM(namely,CEmo-LSTM),which achieves the highest accuracy(around 0.95)when analyzing online Chinese texts.We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1,2019 to March 20,2020 to understand the sentiment evolution of online users during the COVID-19 pandemic.We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions(e.g.,horror and sadness).Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the Sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.

关键词： Sentiment analysis Emoji CEmo-LSTM sentiment evolution COVID-19

Aspect-Based Sentiment analysis for Polarity Estimation of Customer Reviews on Twitter

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Computers, Materials & Continua 2021年第5期67卷 2203-2225页

作者： Ameen Banjar Zohair Ahmed Ali Daud Rabeeh Ayaz Abbasi Hussain Dawood Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21589Saudi Arabia Air University Islamabad44000Pakistan Department of Computer Science Islamabad44000Pakistan Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21589 Saudi Arabia

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 reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based Sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

关键词： Natural language processing Sentiment analysis aspect co-occurrence calculation sentiment polarity customer reviews twitte

Sentiment analysis of Short Texts Based on Parallel DenseNet

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Computers, Materials & Continua 2021年第10期69卷 51-65页

作者： Luqi Yan Jin Han Yishi Yue Liu Zhang Yannan Qian Nanjing University of Information Science&Technology Nanjing210044China State Grid Hunan Electric Power Company Limited Research Institute Changsha410007China Waterford Institute of Technology WaterfordX91 K0EKIreland

Text Sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize Sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.

关键词： Sentiment analysis short texts parallel DenseNet

Text Sentiment analysis Using Frequency-Based Vigorous Features

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China Communications 2019年第12期16卷 145-153页

作者： Abdul Razzaq Muhammad Asim Zulqrnain Ali Salman Qadri Imran Mumtaz Dost Muhammad Khan Qasim Niaz Department of Computer Science MNS-University of AgricultureMultan60600Pakistan University of Management and Technology(UMT) Lahore54000Pakistan NFC Institute of Engineering&Technology Multan60600Pakistan Department of CS&IT The Islamia University of Bahawalpur63100Pakistan Computer Science Department University of AgricultureFaisalabad38000Pakistan Department of Computer Science Institute of Southern PunjabMultan60600Pakistan

Sentiment analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.

关键词： text mining Sentiment analysis sentiment shifters KNN

Fast Sentiment analysis Algorithm Based on Double Model Fusion

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Computer Systems Science & Engineering 2021年第1期36卷 175-188页

作者： Zhixing Lin Like Wang Xiaoli Cui Yongxiang Gu Sanming University Network CenterSanming365004 College of Mathematics and Informatics Fujian Normal UniversityFuzhou350007China Chengdu Institute of Computer Applications Chinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China Sichuan Rainbow Consulting&Software Co. Ltd.Chengdu610041China

Nowadays,as the number of textual data is exponentially increasing,Sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese Sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper,we systematically reviewed the classic and representative works in Sentiment analysis and proposed a simple but efficient optimization.First of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a by-product.Secondly,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive Sentiment analysis.By combining FastText and Bi-LSTM,we have developed a new fast Sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and speed.In particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.

关键词： Sentiment analysis model fusion Bi-LSTM FastText

User Profile System Based on Sentiment analysis for Mobile Edge Computing

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Computers, Materials & Continua 2020年第2期62卷 569-590页

作者： Sang-Min Park Young-Gab Kim Korea University 145 Anam-roSeongbuk-guSeoul02841Korea Sejong University 209Neungdong-roGwangjin-guSeoul05006Korea

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 solution presents a potential risk when handling individual privacy information.Therefore,in order to overcome these problems,we propose a general-purposed user profile system based on Sentiment analysis that extracts individual emotional preferences by comparing the difference between public and individual data based on particular features.The proposed system is built based on a sentiment hierarchy,which is created by using unstructured data on mobile devices.It can compensate for the concentration of single media,and analyze individual private data without the invasion of privacy on mobile devices.

关键词： User profile Sentiment analysis mobile edge computing social network

Optimization of Sentiment analysis Using Teaching-Learning Based Algorithm

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Computers, Materials & Continua 2021年第11期69卷 1783-1799页

作者： Abdullah Muhammad Salwani Abdullah Nor Samsiah Sani Center for Artificial Intelligence Technology Faculty of Information Science&TechnologyUniversiti KebangsaanKajang43000Malaysia

Feature selection and Sentiment analysis are two common studies that are currently being conducted;consistent with the advancements in computing and growing the use of social media.High dimensional or large feature sets is a key issue in Sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features.Furthermore,most reviews from social media carry a lot of noise and irrelevant information.Therefore,this study proposes a new text-feature selection method that uses a combination of rough set theory(RST)and teaching-learning based optimization(TLBO),which is known as RSTLBO.The framework to develop the proposed RSTLBO includes numerous stages:(1)acquiring the standard datasets(user reviews of six major U.S.airlines)which are used to validate search result feature selection methods,(2)preprocessing of the dataset using text processing methods.This involves applying text processing methods from natural language processing techniques,combined with linguistic processing techniques to produce high classification results,(3)employing the RSTLBO method,and(4)using the selected features from the previous process for sentiment classification using the Support Vector Machine(SVM)technique.Results show an improvement in Sentiment analysis when combining natural language processing with linguistic processing for text processing.More importantly,the proposed RSTLBO feature selection algorithm is able to produce an improved Sentiment analysis.

关键词： Feature selection Sentiment analysis rough set theory teachinglearning optimization algorithms text processing

Multi-Class Sentiment analysis of Social Media Data with Machine Learning Algorithms

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Computers, Materials & Continua 2021年第10期69卷 913-930页

作者： Galimkair Mutanov Vladislav Karyukin Zhanl Mamykova Al-Farabi Kazakh National University Almaty050040Kazakhstan

The volume of social media data on the Internet is constantly growing.This has created a substantial research field for data analysts.The diversity of articles,posts,and comments on news websites and social networks astonishes imagination.Nevertheless,most researchers focus on posts on Twitter that have a specific format and length restriction.The majority of them are written in the English language.As relatively few works have paid attention to Sentiment analysis in the Russian and Kazakh languages,this article thoroughly analyzes news posts in the Kazakhstan media space.The amassed datasets include texts labeled according to three sentiment classes:positive,negative,and neutral.The datasets are highly imbalanced,with a significant predominance of the positive class.Three resampling techniques(undersampling,oversampling,and synthetic minority oversampling(SMOTE))are used to resample the datasets to deal with this issue.Subsequently,the texts are vectorized with the TF-IDF metric and classified with seven machine learning(ML)algorithms:naïve Bayes,support vector machine,logistic regression,k-nearest neighbors,decision tree,random forest,and XGBoost.Experimental results reveal that oversampling and SMOTE with logistic regression,decision tree,and random forest achieve the best classification scores.These models are effectively employed in the developed social analytics platform.

关键词： Social media Sentiment analysis imbalanced classes machine learning oversampling undersampling SMOTE russian Kazakh

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment analysis of Twitter Data

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Computers, Materials & Continua 2021年第6期67卷 3451-3461页

作者： Samreen Naeem Wali Khan Mashwani Aqib Ali M.Irfan Uddin Marwan Mahmoud Farrukh Jamal Christophe Chesneau Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Faculty of Applied Studies King Abdulaziz UniversityJeddah21577Saudi Arabia Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Mathematics Universitéde CaenLMNOCampus IIScience 3Caen14032France

This study proposes an approach based on machine learning to forecast currency exchange rates by applying Sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

关键词： Machine learning exchange rate Sentiment analysis linear discriminant analysis principal component analysis simple logistic