Machine Learning-based Sentiment Research Articles

Application of Machine Learning- Based Sentiment Analysis in Packaging Design Style Prediction Modelling

Machine learning-based sentiment analysis plays a pivotal role in the innovative realm of packaging design style prediction modeling. By harnessing advanced algorithms, this approach analyzes consumer sentiments towards various packaging designs, extracting valuable insights into preferences and trends. The model utilizes machine learning techniques to identify patterns in historical data, allowing it to predict and recommend packaging design styles likely to resonate positively with target audiences. This research introduces an innovative approach to packaging design style prediction modeling by incorporating a machine learning-based sentiment analysis technique known as the Conditional Random n-gram Classifier Sentimental (CRn-gCS). Focused on enhancing the intersection of design aesthetics and consumer sentiments, this model employs advanced algorithms to analyze historical data and predict packaging design styles that resonate positively with target audiences. The CRn-gCS, as a key component, refines sentiment analysis by considering conditional relationships between n-grams, contributing to a nuanced understanding of consumer preferences. By leveraging this sophisticated model, designers and marketers can make informed decisions, ensuring that packaging not only aligns with aesthetic trends but also elicits positive emotional responses from consumers. This research contributes to the advancement of predictive modeling in packaging design, offering a comprehensive and data-driven approach to create visually appealing and emotionally resonant packaging.

Deep learning in finance assessing twitter sentiment impact and prediction on stocks

The widespread adoption of social media platforms has led to an influx of data that reflects public sentiment, presenting a novel opportunity for market analysis. This research aims to quantify the correlation between the fleeting sentiments expressed on social media and the measurable fluctuations in the stock market. By adapting a pre-existing sentiment analysis algorithm, we refined a model specifically for evaluating the sentiment of tweets associated with financial markets. The model was trained and validated against a comprehensive dataset of stock-related discussions on Twitter, allowing for the identification of subtle emotional cues that may predict changes in stock prices. Our quantitative approach and methodical testing have revealed a statistically significant relationship between sentiment expressed on Twitter and subsequent stock market activity. These findings suggest that machine learning algorithms can be instrumental in enhancing the analytical capabilities of financial experts. This article details the technical methodologies used, the obstacles overcome, and the potential benefits of integrating machine learning-based sentiment analysis into the realm of economic forecasting.

Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter

This analysis enables the identification and a deeper understanding of the positive and negative sentiments reflected in online conversations, providing a comprehensive view of the direction of public support and preferences regarding presidential candidates. Sentiment analysis through machine learning can manage extensive sentiment data, ensuring time efficiency, and enhancing accuracy in swiftly and comprehensively comprehending people's opinions and preferences. With these advantages, machine learning-based sentiment analysis has gained popularity as an effective choice for understanding people's perspectives, preferences, and responses to various issues and events. Therefore, this research focuses on sentiment analysis regarding public opinions on the 2024 presidential election. The method employed in this research is the SVM algorithm with Word2Vec feature extraction. The researcher is interested in conducting a study related to sentiment analysis of the 2024 Indonesian Presidential election using the Support Vector Machine algorithm because of its high accuracy compared to other algorithms. The use of feature extraction aims to improve the performance and effectiveness of the algorithm, and Word2Vec is chosen because it can represent contextual similarity between two words in the generated vectors, enabling concise and improved text classification based on context. The results of this research indicate the best performance at 80:20 ratio with a precision score of 88,94%, Recall 93.08%, F1-score 90,43% and accuracy of 90,75%. This study's results outperform prior research using the SVM method, which achieved an 82,3% accuracy.

Machine Learning-Based Students’ Sentiment towards E-Learning amid COVID-19 Pandemic

The global COVID-19 pandemic has created exceptional difficulties for college and university students. With educational institutions shutting down and an abrupt shift to online learning, students are facing new challenges. Despite the advantages of e-learning, developing nations like Pakistan are experiencing major hurdles due to a lack of internet access and financial constraints. As a result, students' academic performance is being negatively impacted. Researchers are using sentiment analysis of social media platforms such as Facebook, Twitter, and YouTube to gain insight into student experiences during this challenging time. This study seeks to create a machine learning-based sentiment classification system that can determine the sentiment of higher education students in Pakistan towards e-learning classes during the COVID-19 pandemic. The primary objective of this research is to identify and categorize the challenges and problems that university students face in their e-learning classes. The research employed Multi-Nominal NB and Gaussian NB classifiers to examine the sentiment of online reviews and comments of students. We achieve 99% accuracy using Gaussian NB and 98% accuracy using Multinomial NB classifiers. According to the study's results, the COVID-19 epidemic has had a significant effect on the education system, with the vast majority of feedback being negative (70%). Only a small percentage of feedback was classified as neutral (29.9%) or positive (4%). This finding underscores the urgent need for better solutions for online learning during and after the pandemic. The study underscores the importance of addressing these unique challenges faced by students in developing countries during the pandemic. There is a pressing need for innovative solutions that can ensure that higher education remains effective and accessible in these challenging times. By utilizing sentiment analysis and machine learning, researchers can gain a better understanding of the experiences of students during this crisis. This understanding can inform targeted interventions and solutions that can improve academic performance and well-being for students in developing countries like Pakistan. The findings of this research provide important insights and recommendations for policymakers, educators, and stakeholders in the higher education sector.

Enhancing machine learning-based sentiment analysis through feature extraction techniques.

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework

Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..

Optimizing Machine Learning-based Sentiment Analysis Accuracy in Bilingual Sentences via Preprocessing Techniques

With the recent advances in Natural Language Processing (NLP) technologies, the ability to process, analyze, and understand sentiments expressed in user-generated reviews regarding the products and services they use is becoming more achievable. Despite the latest improvements in this field, little attention has been given to multilingual sentiment analysis. In this article, a framework is presented for sentiment analysis in Arabic and English using two datasets (ASTD, AJGT) along with their translations. Preprocessing techniques, including n-gram tokenization, Arabic-specific stop words removal, punctuation removal, removing repeating characters, parts of speech tagging, stemming, and lemmatization, are applied. Four machine learning classifiers, namely Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM), are employed. We highlight existing specialized research in sentiment analysis for Arabic and English, as well as the employed techniques in each. Furthermore, the impact of preprocessing on accuracy results for both Arabic and English languages is investigated through separate experiments for each step. Experimental results on the ASTD dataset demonstrate close performance across classifiers, with the SVM classifier achieving the highest accuracy of 70%. However, the accuracy varied when using the AJGT dataset, with the NB classifier yielding the best accuracy at approximately 87%. The experiments on the translated datasets from Arabic to English did not exhibit significant differences, although some features performed slightly better using the Arabic datasets.

A Deep Learning-Based Sentiment Analysis Approach for Online Product Ranking With Probabilistic Linguistic Term Sets

The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.

Analysis of the Impact of Vectorization Methods on Machine Learning-Based Sentiment Analysis of Tweets Regarding Readiness for Offline Learning

Twitter users use social media to express emotions about something, whether it is criticism or praise. Analyzing the opinions or sentiments in the tweets that Twitter users send can identify their emotions for a particular topic. This study aims to determine the impact of vectorization methods on public sentiment analysis regarding the readiness for offline learning in Indonesia during the Covid-19 pandemic. The authors labeled sentiment using two different approaches: manually and automatically using the NLP TextBlob library. We compared the vectorization method used by employing count vectorization, TF-IDF, and a combination of both. The feature vectors were then classified using three classification methods: naïve Bayes, logistic regression, and k-nearest neighbor, for both manual and automatic labeling. To assess the performance of sentiment analysis models, we used accuracy, precision, recall, and F1-score for performance metrics. The best results showed that the Logistic regression classifier with the feature extraction technique that combines count vectorization and TF-IDF provided the best performance for both data with manual and automatic labeling.

Data preprocessing approach for machine learning-based sentiment classification

Public sentiment regarding a particular issue, product, activity, or organization can be measured and monitored with an application based on artificial intelligence. The data come from comments circulating on social media. However, the rules for writing comments on social media have yet to be standardized, so non-standard words often appear in these comments. Non-standard words affect the determination of sentiment into positive, negative, and neutral categories. Therefore, this study proposes a data preprocessing approach by inserting the Rabin-Karp algorithm to improve non-standard words. This research consists of several stages, namely crawling data, data preprocessing, feature extraction, model development (based on Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) methods), and analysis of the results. The experimental results showed that the proposed approach influences the determination of the sentiment category composition. Then, model testing results showed that all models obtain the highest value in the Positive category for the precision parameter with a value 1. All models in the Neutral category obtain the highest value for the recall parameter, almost reaching 1. All models in the Neutral category achieve the highest value of the f1-score parameter, with an average value of 0.95. In general, the results of the performance analysis of the classification model showed that the NB and SVM-based models have better performance than the DT method.

Machine Learning-Based Sentiment Analysis of Tweets about COVID-19 Vaccines

The objectives of the study were two-fold: (1) To group mindsets related to COVID-19 vaccinations and examine their distribution by country. Then, based on this distribution, the study aimed to compare the number of vaccinations, deaths, and cases and analyze the relationship between these numbers and the mindset of the society. (2) To analyze people's tweets about the vaccine and compare them with the number of people vaccinated, in order to determine if there was a significant result. The study analyzed data from 17 countries among the top 20 countries with the highest gross national product in 2020. Machine learning methods such as multinomial logistic regression, random forest, naive Bayes, and ridge classification were used to evaluate the performance of predictive models. The accuracy achieved by these models were as follows: naive Bayes (76%), random forest (85.03%), ridge classification (85.72%), and multinomial logistic regression (86.67%). In conclusion, the study found that with increasing vaccination rates, positive interpretations of vaccines differed more than other moods. The study contributes to advancing awareness of the public's perception of COVID-19 vaccinations and supports the goal of eliminating coronavirus from the planet.

Understanding People Opinion on Artificial Intelligence Ethics through Machine Learning-based Sentiment Analysis

Artificial Intelligence (AI) ethics is so near to human existence. It contains a collection of beliefs, concepts, and methods that utilize generally recognized moral standards to govern moral behavior in developing and using AI technology. Besides providing many advantages to people, the development and use of AI also pose risks that may harm the humankind. At the crossroads between the need for AI and the risks, we want to know what people think about AI. For that purpose, we devised a sentiment analysis system powered by Naive Bayes Classifier and Term Frequency-Inverse Document Frequency (TF-IDF) methods. After analyzing 1.138 data taken from YouTube and Twitter which it is categorized into three opinion labels, namely positive, neutral, and negative, the system can achieve accuracy of 71% with average precision of 66%, average recall of 71%, and average F1-Score of 64% through K-Fold Cross-Validation.

Does Local Government Competition Reduce Environmental Governance Performance? The Role of Public Value Conflict and Media Sentiment

With the increasing attention paid to environmental protection and sustainable development in various countries worldwide, the relationship between local government competition and environmental governance has become more subtle and complex. This paper provides new insight into their relationship based on public value theory and media sentiment perspective. Utilizing panel data from 2012 to 2019 in 216 cities in China, this study integrated Data Envelopment Analysis, Conflicting Attitudes Model, Computer-Aided Text Analysis, and machine learning-based sentiment analysis, as well as nonlinear mediation model to empirically test the relationships among local governments’ competition pressure, public value conflict, media sentiments, and environmental governance performance. The study found that: (1) Competition pressure and environmental governance performance exist in a “U-curved” relationship. (2) The core mechanism of the above relationship lies in the mediating role of public value conflict. Within a specific range, the public value conflict faced by local governments increases as competition pressure increases. This conflict would push local governments into a dilemma and induce them to commit misconduct. However, when competition pressure exceeds this range, the public value conflict faced by local governments will be weakened, leading environmental governance performance to rebound. (3) Negative media sentiments significantly alleviate the negative impact of public value conflict on environmental governance performance. This study helps researchers and policymakers recognize government competition’s influence on environmental governance from a public value perspective, with further exploration and confirmation of the moderating role of media sentiments. It also provides theoretical and policy enlightenment for rethinking the behavior logic of local government and solving the dilemma of local government environmental governance.

Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era

Since trading has been transformed into online platforms, marketing strategies have adapted to digital systems in order to enhance the Customer Relationship Management (CRM) in the E-commerce era. E-commerce systems are the most widely used digital platforms where customer information including personal, and behavioral information, flows as a big data stream. Conducting business intelligent observation on digital big data assists to improve digital marketing policy through the customer intention prediction, decision-making to advertise based on the target group clustering, and customer assist recommendation. To discover the business intelligent, sentiment analysis technology can assist as a solution to understand the customer behavior through the opinion mining where the natural language processing, text analysis, computational linguistics, and biometrics are conducted to analysis the customer information and feedbacks, for smart digital marketing applications. This research observes the applications of sentiment analysis in E-commerce systems as a comprehensive study, and the critical role of discovering business intelligent for smart digital marketing in E-commerce platforms is pointed out according to the technical perspective. Furthermore, the concept of a business intelligent framework integrated with the modelling of decision-making, prediction, and recommendation systems using the contribution of hybrid feature selection which is based on rule-based and machine learning-based sentiment analysis, is proposed for the future innovative smart digital marketing trend.

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

On Twitter, COVID-19 is a highly discussed topic. People worldwide have used Twitter to express their viewpoints and feelings during the pandemic. Previous research has focused on particular topics such as the public’s sentiment during the lockdown, their opinion on governmental measures, or their stance towards COVID-19 vaccines. However, until today, there is no comprehensive overview that presents possible areas of application for sentiment analysis of COVID-19 Twitter data. Therefore, this study reveals how sentiment analysis can provide relevant insights for managing the pandemic by applying a behavioral and social science lens. In this context, our systematic literature review focuses on machine learning-based sentiment analysis techniques and compares the best-performing classification algorithms for COVID-19-related Twitter data. We performed a search in five databases, which are: IEEE Xplore DL, ScienceDirect, SpringerLink, ACM DL, and AIS Electronic Library. This search resulted in 40 papers published between October 2019 and January 2022 that used sentiment analysis to evaluate the public opinion on COVID-19-related topics, which we further investigated. Our research indicates that the best performing models in terms of accuracy are ensemble models that comprise various machine learning classifiers. Especially BERT and RoBERTa models provide the most promising results when fine-tuned on Twitter data. Our study aims to combine machine learning-based sentiment analysis and insights from social and behavioral science to provide decision-makers and public health experts with guidance on the application of sentiment analysis in the fight against the spread of COVID-19.

Enhanced Elman spike neural network based sentiment analysis of online product recommendation

The major task of Natural Language Processing (NLP) is Sentiments Analysis (SA) or Opinion Mining (OM). It captures the opinion, trust, and feelings of every user’s about the related products to find whether the attitude of users is positive, negative, or neutral. By this, companies can make necessary changes in the product for customer satisfaction. The majority of the existing approaches on sentiment analysis have inaccurate and takes more time. Therefore, in this manuscript, an Enhanced Elman Spike Neural Network based Sentiment Analysis of Online Product recommendation is proposed (EESNN-SA-OPR). Here, filtering Collaborative (FC) and product to product (P–P) similarity are used as new recommendation systems. The aim of Collaborative Filtering is “to predict the best shops and product–product​ similarity is to predict the best products”. Initially, the datas are taken from Amazon reviews database. Then the input data is pre-processed. The trilateral smoothing filtering (TRSF) is used to remove the content which is no more needed and texts related filtering. After that, the features like manufacturing price (MRP), Manufacturing date (MFD), discounts, offers, ratings in qualities are extracted by using Dominant Gradient Local Ternary Pattern descriptor (DGLTPD) technique. At last, enhanced Elman spike neural network classifies the product recommendation as excellent, good, very good, bad and very bad. The proposed method is executed in MATLAB and its performance is analyzed under some performance metrics, like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), accuracy, F-Score, recall and precision. The proposed EESNN-SA-OPR method provides 23.14%, 15.96%, 31.54% higher accuracy and 12.33%, 21.31%, 41.09% lower mean absolute error compared with the existing techniques, like Sentiment analysis of online product reviews utilizing DLMNN and future prediction of online product utilizing IANFIS (DLMNN-SA-OPR), a machine learning-based sentiment analysis of online product reviews with new term weighting along feature selection method (LSIBA-ENN-SA-OPR) and Sentiment Analysis on the Reviews of Online Product utilizing Optimized RNN-LSTM along Support Vector Machine (RNN-LSTM-SA-OPR) respectively.

Impact of demography on linguistic aspects and readability of reviews and performances of sentiment classifiers

This study investigates how user demographics influence the linguistic characteristics and readability of the reviews and the performance of sentiment classification methods. Two restaurant review datasets representing consumers of distinct demographics, such as English language fluency levels, socio-culture, and geographic locations, are compared. It is observed that demographic dissimilarities affect various linguistic attributes, such as sentence structure and vocabulary usage of reviews. The Mann–Whitney U test indicates significant differences in these two sets of reviews on various linguistic aspects. Regarding the readability of the two corpora, no noticeable difference is perceived by Flesch Reading Ease (FRE) test. The discrepancy in the results of lexicon-based and machine learning-based sentiment classification methods in two corpora suggests that demography has an influential role in the performance of sentiment classifiers.

A novel data processing approach to detect fraudulent insurance claims for physical damage to cars

Some automobile insurance companies use computerized auto-detection systems to expedite claims payment decisions for insured vehicles. Claims suspected of fraud are evaluated using empirical data from previously investigated claims. The main objective of this manuscript is to demonstrate a novel data processing system and its potential for use in data classification. The data processing approach was used to develop a machine learning-based sentiment classification model to describe property damage fraud in vehicle accidents and the indicators of fraudulent claims. To this end, Singular Value Decomposition-based components and correlation-based composite variables were created. Machine learning models were then developed, with predictors and composite variables selected based on standard feature selection procedures. Five machine learning models were used: Boosted Trees, Classification and Regression Trees, Random Forests, Artificial Neural Networks, and Support Vector Machines. For all models, the models with composite variables achieved higher accuracy rates, and among these models, the artificial neural network was the model with the highest accuracy performance at 76.56%.

Machine learning-based sentiment analysis of Gujarati reviews

Opinion examination is the investigation of applied information in an articulation, like appraisals, assessments, sentiments, or perspectives toward a point, individual, or component. Positive, negative, and unbiased articulations are altogether conceivable. The authors of this exploration have built a dataset of Gujarati film audits and give the discoveries produced by the proposed calculation message in the wake of performing sentiment examination utilising a five different machine classifier. The authors fostered various datasets to test our calculation's capacities with different machine classifiers. This paper clarifies how information was gathered to shape a dataset, as well as Gujarati text pre-handling, include determination, and order techniques. According to the results of the investigation, all of the classifiers are performing brilliantly, generating overall precision greater than 75%, however KNN is unable to produce preferred precision above the others.

Machine learning-based sentiment analysis of Gujarati reviews

Machine learning-based sentiment analysis of Gujarati reviews