Sentiment Analysis Approach Research Articles

COVID-19 pandemic & cyber security issues: Sentiment analysis and topic modeling approach

The purpose of this study is to understand public awareness of cybersecurity-related issues and discussions during the COVID-19 pandemic. Employees and confidential organizational data are vulnerable to cyber-attacks as a result of the pandemic, which has raised concerns about cyber security about the new normal of working from home. The public’s main sentiments and aspects related to cyber security concerns have been mined from tweets on the microblogging social media website Twitter. Sentiment Analysis and Topic Modeling techniques have been applied to understand the perspectives, emotions, and themes discussed by people. The analysis reveals people are becoming more aware of cybercrime-related concerns and are more positive than negative in combating the challenge of cybercrime. The paper also highlights the main themes revealed by Topic Modeling.

The effect of technical and functional quality on online physician selection: Moderation effect of competition intensity

In this study, we use a hybrid sentiment analysis approach to identify and assess service quality dimensions (i.e., technical and functional quality) in unstructured textual reviews and employ econometric methods to theorize and test the impact of these service quality signals on online physician selection. By analyzing a data set with 246,294 reviews based on 5,452 physicians in 8 disease markets from an online health consultation platform, this study reports that technical and functional quality cues are important market signals used by patients to select online physicians. More importantly, this study provides strong evidence that the effects of these signals on online physician selection are substantially contingent on disease market competition intensity. Furthermore, this market competition effect is moderated by the level of disease risk. This study contributes insight into patients' physician choice behavior by introducing the role of market-level characteristics. It also guides online health consultation platforms in developing operational strategies to increase patient engagement.

Sentiment classification and analysis of twitter data on distance learning using ML classifiers

Online education comes in shades of grey. We analyze public view on the pandemic in regards with mental stress, interrupted power supply, affordability and access to internet, flexibility of schedule, reduction of long-distance commute, risk of covid and other such consequences of online learning and Government’s take on the need for inclusive education policies. In this paper, we qualitatively inspect the consequences of COVID-19 pandemic on education of the students. This study primarily focuses on the response of students of all age groups, educators, college professors, school teachers and also parents of young students towards the approach of distance learning or Online education in the past two years. We have taken two datasets, first being the Twitter dataset comprising of tweets from around the whole world and second, dataset which is specific to tweets from India. The data has been extracted from twitter with the aid of twitter API and then two sentiment analysis approaches have been implemented, first Machine learning classifiers namely, Naïve Bayes, SVM, Random Forest, Logistic Regression, KNN, XG-Boost and secondly, Lexicon Based algorithms, VADER and TEXTBLOB. Upon performing the said approaches, the maximum accuracy achieved is 94%.

Exploring National Park Visitors’ Judgements from Social Media: The Case Study of Plitvice Lakes National Park

This study aims to conduct a survey of visitor reviews of the Plitvice Lakes National Park in Croatia to detect strengths and weaknesses of the park. In total, 15,673 reviews written in the period between 2007 and 2021 were scraped from the social media platform TripAdvisor. The research applies a comprehensive combination of multidimensional scaling, sentiment analysis, and natural language processing approaches to a sample area of international naturalistic interest. Analyzing the opinions of visitors, the authors identify: the main topics of interest related to the management of the park; and the strengths and weaknesses on the basis of definitely positive and decidedly negative reviews, respectively. The tested methodology is easily applicable for the analysis of different naturalistic contexts and protected areas, even in different countries, thanks to the use of translated reviews. The results obtained show that visitors to protected natural areas are not only interested in naturalistic and landscape aspects but also in issues such as accessibility and management of routes and visits.

A Study on Diverse Methods and Performance Measures in Sentiment Analysis

: With the vast development of internet technology 2.0, millions of people are sharing their opinions on different social networking sites. To obtain the necessary information from the huge volume of user-generated data, the attention on sentiment analysis among the research community is growing. Growth and prominence of sentiment analysis is synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps to produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of user-generated content such as noisy, irrelevant information and fake reviews, there is a significant demand for an effective methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies and highlights the challenges and performance factors of various approaches of sentiment analysis including text preprocessing, opinion spam detection, and aspect level sentiment analysis. Background: User-generated content is growing all over the globe and people more eagerly express their views on social media towards various aspects. The opinionated text is difficult to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the usergenerated data. Objective: The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis namely text pre-processing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.

A New Ontology-Based Method for Arabic Sentiment Analysis

Arabic sentiment analysis is a process that aims to extract the subjective opinions of different users about different subjects since these opinions and sentiments are used to recognize their perspectives and judgments in a particular domain. Few research studies addressed semantic-oriented approaches for Arabic sentiment analysis based on domain ontologies and features’ importance. In this paper, we built a semantic orientation approach for calculating overall polarity from the Arabic subjective texts based on built domain ontology and the available sentiment lexicon. We used the ontology concepts to extract and weight the semantic domain features by considering their levels in the ontology tree and their frequencies in the dataset to compute the overall polarity of a given textual review based on the importance of each domain feature. For evaluation, an Arabic dataset from the hotels’ domain was selected to build the domain ontology and to test the proposed approach. The overall accuracy and f-measure reach 79.20% and 78.75%, respectively. Results showed that the approach outperformed the other semantic orientation approaches, and it is an appealing approach to be used for Arabic sentiment analysis.

How Do People Respond to Local Governments in Communicating Government Programs Through Social Media?

Open communication is one of the government's efforts to meet the demands of society in creating more transparent public information services. Instagram is one of the social media that allows the government and the public to interact through two-way communication, creating and sharing content in the form of words, images, audio or video, in real time. This study aims to identify the community's response to the communication of local leaders in informing government programs through Instagram. This study focuses on the Indonesian government's program on the issue of public education through the New Student Admission program. This study used an exploratory sequential mixed-method. The data was obtained from public comments via Instagram on posts submitted by the head of the West Java region. A total of 867 comments are analyzed in this study. Data processing techniques using text mining with sentiment analysis approach. The study found that the activeness of local leaders in using Instagram to inform government programs was very effective and able to attract participation from the community. The majority of comments showed negative sentiment towards government program information regarding the new student admission program which was conveyed through the West Java regional head's Instagram.
 
 Keywords: open communication, Instagram, participation, public services, transparency

Identifying Labor Market Competitors with Machine Learning Based on Maimai Platform

ABSTRACT The demand for skilled labor has increased dramatically in the current knowledge-based economy, which is characterized by the growing intensity in labor market competition between firms. Therefore, it would be of special interest to identify future labor market competitors. At present, with the vast amount of textual data, the existing study focuses on constructing the human capital overlap and product overlap metrics with the text data as predictors to predict the labor market competition in the United States. Based on these metrics, this paper experiments with machine learning methods to predict Chinese labor market competition with Chinese text data. Furthermore, sentiment analysis is becoming popular and it has been used in a wide variety of fields. However, due to lack of data, there are few existing studies using sentiment analysis approach of firms’ online reviews. In response to this research gap, this paper constructs the sentiment analysis metric by mining the emotional content expressed in the firms’ online reviews on Maimai’s platform. The results show that our proposed metrics have superior predictive power over conventional measures and highlight the predictive utility of proposed sentiment analysis metric. Moreover, the nuanced two-dimensional competition analysis gives some interesting results.

Fine-grained, aspect-based sentiment analysis on economic and financial lexicon

Extracting sentiment from news text, social media and blogs has recently gained increasing interest in economics and finance. Despite many successful applications of sentiment analysis (SA) in these domains, the range of semantic techniques employed is still limited and predominantly focused on the detection of sentiment at a coarse-grained level. This paper proposes a novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis. The aim is to identify the sentiment associated with specific topics of interest in each sentence of a document and to assign real-valued polarity scores between -1 and +1 to those topics. The proposed approach is unsupervised and customised to the economic and financial domains by using a specialised lexicon provided by us along with the FiGAS source code. Our lexicon-based SA approach relies on a detailed set of semantic polarity rules that allow understanding of the origin of sentiment – in the spirit of the recent trend on Interpretable AI. We provide an in-depth comparison of the performance of the FiGAS algorithm with other popular lexicon-based SA approaches in predicting a humanly annotated dataset in the economic and financial domains. Our results indicate that FiGAS statistically outperforms the other methods by providing a sentiment score that is closer to those of human annotators.

Machine learning approaches for sentiment analysis

Sentiment Analysis or Opinion Mining is popular task of Natural Language Processing (NLP) performed on textual data generated by users to know the orientation or sentiment of the text. To perform Sentiment Analysis, it is critical to create an accurate and precise model, machine learning techniques are heavily utilized to build an accurate model. Deep learning and transfer learning techniques have been found to have increased utilization and better results, making them one of the most popular research areas around the world. Hotel and restaurant industries analyze reviews to obtain a deeper understanding of their client’s needs, likes and dislikes, whereas specialists use Twitter data and stock market news items to forecast stock market trends. Machine Learning algorithms are most essential part of a Sentiment Analysis model, this survey paper analyze all the widely used Machine Learning Approaches for Sentiment Analysis. A brief introduction on Methodology for Sentiment Analysis is given along with conclusion and future scope and in the field of Sentiment Analysis.

Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

The emissions of greenhouse gases, such as carbon dioxide, into the biosphere have the consequence of warming up the planet, hence the existence of climate change. Sentiment analysis has been a popular subject and there has been a plethora of research conducted in this area in recent decades, typically on social media platforms such as Twitter, due to the proliferation of data generated today during discussions on climate change. However, there is not much research on the performances of different sentiment analysis approaches using lexicon, machine learning and hybrid methods, particularly within this domain-specific sentiment. This study aims to find the most effective sentiment analysis approach for climate change tweets and related domains by performing a comparative evaluation of various sentiment analysis approaches. In this context, seven lexicon-based approaches were used, namely SentiWordNet, TextBlob, VADER, SentiStrength, Hu and Liu, MPQA, and WKWSCI. Meanwhile, three machine learning classifiers were used, namely Support Vector Machine, Naïve Bayes, and Logistic Regression, by using two feature extraction techniques, which were Bag-of-Words and TF–IDF. Next, the hybridization between lexicon-based and machine learning-based approaches was performed. The results indicate that the hybrid method outperformed the other two approaches, with hybrid TextBlob and Logistic Regression achieving an F1-score of 75.3%; thus, this has been chosen as the most effective approach. This study also found that lemmatization improved the accuracy of machine learning and hybrid approaches by 1.6%. Meanwhile, the TF–IDF feature extraction technique was slightly better than BoW by increasing the accuracy of the Logistic Regression classifier by 0.6%. However, TF–IDF and BoW had an identical effect on SVM and NB. Future works will include investigating the suitability of deep learning approaches toward this domain-specific sentiment on social media platforms.

MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.

A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews

User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.

Characterizing discourses about COVID-19 vaccines on Twitter: a topic modeling and sentiment analysis approach

ABSTRACT Background Evidence-based health communication is crucial for facilitating vaccine-related knowledge and addressing vaccine hesitancy. To that end, it is important to understand the discourses about COVID-19 vaccination and attend to the publics’ emotions underlying those discourses. Methods We collect tweets related to COVID-19 vaccines from March 2020 to March 2021. In total, 304,292 tweets from 134,015 users are collected. We conduct a Latent Dirichlet Allocation (LDA) modeling analysis and a sentiment analysis to analyze the discourse themes and sentiments. Results This study identifies seven themes of COVID-19 vaccine-related discourses. Vaccine advocacy (24.82%) is the most widely discussed topic about COVID-19 vaccines, followed by vaccine hesitancy (22.29%), vaccine rollout (12.99%), vaccine facts (12.61%), recognition for healthcare workers (12.47%), vaccine side effects (10.07%), and vaccine policies (4.75%). Trust is the most salient emotion associated with COVID-19 vaccine discourses, followed by anticipation, fear, joy, sadness, anger, surprise, and disgust. Among the seven topics, vaccine advocacy tweets are most likely to receive likes and comments, and vaccine fact tweets are most likely to receive retweets. Conclusions When talking about vaccines, publics’ emotions are dominated by trust and anticipation, yet mixed with fear and sadness. Although tweets about vaccine hesitancy are prevalent on Twitter, those messages receive fewer likes and comments than vaccine advocacy messages. Over time, tweets about vaccine advocacy and vaccine facts become more dominant whereas tweets about vaccine hesitancy become less dominant among COVID-19 vaccine discourses, suggesting that publics become more confident about COVID-19 vaccines as they obtain more information.

Incorporating sentimental analysis into development of a hybrid classification model

The Sentimental Analysis approach is typically used for analyzing a user's ideas, sentiments, and text subjectivity, all of which are expressed through text. Sentimental analysis, also known as "opinion mining," is a type of data mining that follows the concept of emotional analysis presented by people in a thoughtful manner. Based on historical evidence, websites are the most effective venue for soliciting customer feedback. Existing methodologies based on sentimental analysis are ineffective. As a result, a novel hybrid framework based on three classifiers, including SVM, logistic regression, and random forest, is proposed in this paper. Based on user feedback or historical data, the hybrid model serves as an effective classifier, assisting in the development of more accurate classification results. Furthermore, the proposed model has worked well and has been compared to other methods based on several performance metrics, such as accuracy, precision, recall, and recall.

BILEAT: a highly generalized and robust approach for unified aspect-based sentiment analysis

BILEAT: a highly generalized and robust approach for unified aspect-based sentiment analysis

Discovering signals of platform failure risks from customer sentiment: the case of online P2P lending

PurposeDigital platforms have grown significantly in recent years. Although high platform failure risks (PFR) have plagued the industry, the literature has only given this issue scant treatment. Customer sentiments are crucial for platforms and have a growing body of knowledge on its analysis. However, previous studies have overlooked rich contextual information emb`edded in user-generated content (UGC). Confronting the research gap of digital platform failure and drawbacks of customer sentiment analysis, we aim to detect signals of PFR based on our advanced customer sentiment analysis approach for UGC and to illustrate how customer sentiments could predict PFR.Design/methodology/approachWe develop a deep-learning based approach to improve the accuracy of customer sentiment analysis for further predicting PFR. We leverage a unique dataset of online P2P lending, i.e., a typical setting of transactional digital platforms, including 97,876 pieces of UGC for 2,467 platforms from 2011 to 2018.FindingsOur results show that the proposed approach can improve the accuracy of measuring customer sentiment by integrating word embedding technique and bidirectional long short-term memory (Bi-LSTM). On top of that, we show that customer sentiment can improve the accuracy for predicting PFR by 10.96%. Additionally, we do not only focus on a single type of customer sentiment in a static view. We discuss how the predictive power varies across positive, neutral, negative customer sentiments, and during different time periods.Originality/valueOur research results contribute to the literature stream on digital platform failure with online information processing and offer implications for digital platform risk management with advanced customer sentiment analysis.

Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach

BackgroundSince January 2020, India has faced two waves of COVID-19; preparation for the upcoming waves is the primary challenge for public health sectors and governments. Therefore, it is important to forecast future cumulative confirmed cases to plan and implement control measures effectively. MethodsThis study proposed a hybrid autoregressive integrated moving average (ARIMA) and Prophet model to predict daily confirmed and cumulative confirmed cases. The built-in auto.arima function was first used to select the optimal hyperparameter values of the ARIMA model. Then, the modified ARIMA model was used to find the best fit between the test and forecast data to find the best model parameter combinations. Articles, blog posts, and news stories from virologists, scientists, and health experts related to the third wave of COVID-19 were gathered using the Python web scraping package Beautiful Soup. Their opinions (sentiments) toward the potential third wave were analyzed using natural language processing (NLP) libraries. ResultsA spike in daily confirmed and cumulative confirmed cases was predicted in India in the next 180 days based on past time series data. The results were validated using various analytical tools and evaluation metrics, producing a root mean square error (RMSE) of 0.14 and a mean absolute percentage error (MAPE) of 0.06. The NLP processing results revealed negative sentiments in most articles and blogs, with few exceptions. ConclusionThe findings of this study suggest that there will be more active cases in the upcoming days. The proposed models can forecast future daily confirmed and cumulative confirmed cases. This study will help the country and states plan appropriate public health measures for the upcoming waves of COVID-19.

Product selection based on sentiment analysis of online reviews: an intuitionistic fuzzy TODIM method

Online reviews contain a great deal of information about consumers' purchasing preferences, which seriously affects potential consumers' purchasing decisions. Using the online review data to help customers make purchasing decisions has become a concern of customers, which has theoretical and practical application value. Therefore, a product selection model is presented based on sentiment analysis combined with an intuitionistic fuzzy TODIM method. Firstly, the product features are extracted by the Apriori algorithm based on online reviews. The sentiment orientation and intensity of the sentiment words for the product features are identified by the lexicon-based sentiment analysis approach. Next, the sentiment orientation of the product features is represented by an intuitionistic fuzzy value. Then the intuitionistic fuzzy TODIM method is used to determine the ranking results of the alternative products. Finally, the case study of mobile phone selection is given to illustrate the proposed approach. The results show that the proposed method considers the online reviews’ sentiment orientation and intensity and the consumers’ gain and loss in the purchasing product process and is more reasonable than the previous research.

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

Nowadays, social media platforms have become a mirror that imitates opinions and feelings about any specific product or event. These product reviews are capable of enhancing communication among entrepreneurs and their customers. These reviews need to be extracted and analyzed to predict the sentiment polarity, i.e., whether the review is positive or negative. This paper aims to predict the human sentiments expressed for beauty product reviews extracted from Amazon and improve the classification accuracy. The three phases instigated in our work are data pre-processing, feature extraction using the Bag-of-Words (BoW) method, and sentiment classification using Machine Learning (ML) techniques. A Global Optimization-based Neural Network (GONN) is proposed for the sentimental classification. Then an empirical study is conducted to analyze the performance of the proposed GONN and compare it with the other machine learning algorithms, such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). We dig further to cross-validate these techniques by ten folds to evaluate the most accurate classifier. These models have also been investigated on the Precision-Recall (PR) curve to assess and test the best technique. Experimental results demonstrate that the proposed method is the most appropriate method to predict the classification accuracy for our defined dataset. Specifically, we exhibit that our work is adept at training the textual sentiment classifiers better, thereby enhancing the accuracy of sentiment prediction.