Results Of Sentiment Analysis Research Articles

Enhancing XGBoost Classification with SVM-SMOTE & EasyEnsemble for Imbalanced Telemedicine Sentiment Data

Telemedicine is the practice of health through applications using audio, visual and data communication, including care, diagnosis, consultation and treatment as well as remote medical data exchange. Based on the results of sentiment analysis on telemedicine applications, imbalance data is often found. The purpose of this research is to identify the use of SVM-SMOTE and EasyEnsemble in improving the performance of XGBoost classification on sentiment data imbalance in Telemedicine. Identification is done by including SVM-SMOTE and EasyEnsemble methods in improving XGBoost Classification Performance using data obtained from the Halodoc application, then validation techniques will be carried out using AUC and GMeans. The results showed that the use of SVM SMOTE and EasyEnsamble for data imbalance in XGBoost obtained the best model that is feasible to use in improving the performance of imbalance classification of sentiment data in health applications.

Identify Issues That Influence Customer Satisfaction Using Sentiment Analysis and Topic Modeling

The development of internet users in Indonesia continues to increase over time, this makes customers comfortable with digital transactions. Trinusa Travelindo Company or better known as Traveloka, which is engaged in the online travel agent sector, has proven its extraordinary achievement, namely becoming the E-tourism application that is ranked 1st most visited by the Indonesian people in 2022. However, these results are also directly proportional to the number of negative reviews created in the application review itself. This study aims to identify what issues affect customer satisfaction with topic modelling. This study uses the text mining method derived from the results of Traveloka application user reviews. The data source obtained in this study is secondary data by using the Traveloka application review data crawling technique with samples from January to May. Based on sentiment analysis, positive sentiment is more dominant (50.38%) compared to negative sentiment (49.62%). User reviews are grouped based on the results of sentiment analysis, then Topic Modelling is carried out to find out what issues affect customer satisfaction. The topic modelling of the data used is Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) with the help of phyton 6.3. to find out what words and topics often appear or as factors that influence customer satisfaction. The most influential negative issues include the refund and payment process, pay later services, and the ordering experience which is considered complicated. On the other hand, issues that contribute positively to customer satisfaction include product quality, service quality, and promos and discounts. This is reinforced by the results of topic modelling which show that these aspects are often the main concerns in user reviews on the Google Play store. Thus, this study suggests that in order to increase customer satisfaction, Traveloka needs to focus on improving the quality of service, products, and offering more attractive promos, while simplifying the refund process and ordering experience in the application, some still say it is complicated.

Exploring satisfaction of amusement parks in Indonesia

The rapid growth of amusement parks in Indonesia has elevated them to significant tourist hubs, prompting a detailed examination of visitor satisfaction within these settings. This study aims to analyze the satisfaction trends among tourists visiting the top 11 amusement parks in Indonesia and assess whether their ratings accurately reflect their satisfaction. Moreover, it investigates the factors influencing satisfaction levels, employing an adjusted rating method to dissect tourists' ratings and compare them with sentiment analysis results derived from provided reviews. While acknowledging the fluctuating performance of these destinations, with most showing signs of improvement, the study highlights that only 5 destinations within the sample exhibit comovement between adjusted ratings and sentiment analysis results. Furthermore, a closer inspection reveals that some destinations have successfully minimized the prevalence of negative sentiments expressed in reviews. However, there is still 1 destination that is of concern because it has decreased customer satisfaction as indicated by a decrease in score and the addition of negative sentiment.

Sentiment Analysis Towards the Film Dirty Vote on Twitter Social Media Using the K-Nearest Neighbor Algorithm

The appearance of the dirty vote film has received public attention and went viral on social media after being released and watched by millions of people in a short time. The dirty vote film has become a topic of discussion, one of which is on the social media platform Twitter. This research was conducted to determine the views or tendencies of public opinion regarding dirty vote films on Twitter social media using K-Nearest Neighbor which will be classified into positive, neutral and negative sentiment. The sentiment data that was collected in the data crawling process was 4000 pieces of data. Then after preprocessing there were 3978 data. Labeling was carried out using text blob, it was found that the negative sentiment class was 3470 superior to the positive sentiment class of 451 and the neutral sentiment class was 57. The 10-fold cross validation test produced an average accuracy value of 87.5%. Testing was carried out with 80% training data consisting of 3182 data and 20% test data consisting of 796 test data. The results of sentiment analysis show that the K-Nearest Neighbor method can be used for sentiment analysis. The accuracy value obtained was 87%, precision was 87%, recall was 100%, and f1-score was 93%.

Analisis Sentimen Media Sosial Terhadap Aplikasi Perbankan Untuk Mengetahui Kepuasan Pengguna Aplikasi: Studi Kasus Pada Livin by Mandiri dan BCA Mobile

In this study, the researchers aimed to explore how sentiment expressed on social media platforms towards e-servqual provided by Livin' by Mandiri and BCA Mobile applications. This research uses sentiment analysis by collecting data from Twitter. Naïve Bayes is the method chosen for this research due to its user-friendly nature and ability to handle large datasets. Regarding the results of sentiment analysis on the e-servqual dimensions for the BCA Mobile application, overall opinions tend to be negative, although there are some positive opinions, especially on the variables of user friendliness, personal nedss, and efficiency. However, sentiments towards the dimensions of site organization, responsiveness, and reliability are mostly filled with negative sentiments. In contrast, sentiments towards the Livin by Mandiri application mostly contain positive sentiments. In the e-servqual dimension, Personal Needs, user frendlines and Responsiveness, and Site Organization all show positive sentiments, while, Efficiency, and Reliability show negative sentiments. In addition, based on the results of sentiment analysis, this research also examines topic modeling which is used to see what topics often appear in both applications.

Extracting method for fine-grained emotional features in videos

Multimodal Sentiment Analysis (MSA) has significant applications in social media analysis and healthcare. It utilizes features from multiple modalities e.g., video, general text, acoustics, and vision, to obtain more credible sentiment analysis results. However, previous studies have ignored substantial variations in sub-features emerging within both the acoustic and visual modalities during feature extraction. Instead, they primarily rely on a simple concatenation method to derive representations. Consequently, these approaches prevent feature extractors from effectively leveraging non-verbal (acoustic and visual) features, thereby, constraining the model’s overall performance. To solve this problem, this study proposes a method for extracting fine-grained emotional features from videos. By segregating the initial features of non-verbal modalities into distinct domains, then separately modeling and uniformly re-integrating them, our method effectively exploits the modality-specific information in these original features. Through extensive experiments, the performance of all models significantly improves using the features extracted following our method compared with original ones. This substantiates that our proposed approach more effectively utilizes features from non-verbal modalities compared with conventional approaches. This also underscores that processing non-verbal sub-features separately before integration represents a viable solution for enhancing the performance of the MSA model.

Comparison Study on Sentiment Analysis Using Lexicon for Airlines Using Supervised Methods

Nowadays, sentiment analysis usually uses social media websites such as Twitter to analyse the public's opinion on a particular topic. Users have unrestricted access to this website and can express their opinions freely without any restrictions, and it is well-known that opinions influence readers. Therefore, the main objective of this research is to identify the public's positive, negative, and neutral attitudes towards airlines such as Malaysian Airlines, Air Asia, and Malindo Air. Two approaches are adopted: the lexicon-based approach to label the tweets and the machine learning approach such as Naïve Bayes, SVM, and Deep Learning to predict and compare the performance. A total of 35,005 tweets from airlines with all three keywords were evaluated. Deep Learning achieved the highest accuracy and f1 score with 74.10% and 73.49%, respectively. The results show that Deep Learning outperforms the other classifiers by having the highest precision and f1 score. Finally, the sentiment analysis results are visualized in a dashboard to enable a more accurate research analysis. For future work, the dashboard could be integrated into a web-based dashboard to be published for the public and not only for airlines.

Analisis Sentimen Larangan Impor Pakaian Bekas Menggunakan Metode Support Vectore Machine dan Lexicon Based

X as a social media platform, provides real-time tweets after the event, making it very relevant for sentiment analysis related to certain topics. This research discusses the prohibition on imports of used clothing in Indonesia using the SVM and Lexicon-based methods. The research aims to determine public sentiment regarding this government policy. The SVM method achieved 85.87% accuracy, with 93.83% recall for positive sentiment and 62.00% recall for negative sentiment. There were 76 wrong positive predictions with a precision of 88.11% and 37 wrong negative predictions with a precision of 77.02%. Meanwhile, the Lexicon Method achieved 60% accuracy, with a positive precision of 69% and a negative precision of 31%. Recall for the negative class is 25%, while for the positive class it is 75%. The results of sentiment analysis applying the Support Vector Machine method to build a classification model resulted in 753 data being successfully classified as positive sentiment, while 147 data were classified as negative sentiment. With different accuracies, it shows that sentiment analysis using the Support Vector Machine method has a higher level of accuracy than Lexicon.

Sentiment Analysis of FLO Applications For Women's Needs Using The CNN And LSTM Algorithms.

One application that helps women is an application that can track menstrual schedules, plan pregnancy and track pregnancy schedules until the estimated time of delivery. An example of an application that is widely used on the Google Play Store is FLO. FLO application that has been downloaded more than 100 million times and has been reviewed as many as 2 million reviews on the Google Play Store seen on January 12, 2023. Sentiment Analysis is an ongoing research field in the field of text mining and also the computational treatment of opinions, sentiments and subjectivity text that can be used as an evaluation of an application. The method chosen in this sentiment analysis research is CNN and RNN with Long Short Term Memory (LSTM) variants. In this study, the data used to carry out sentiment analysis is review data in text form. The results of sentiment analysis with the research object of FLO application reviews were 12,000 review data selected by country, namely Indonesia, which had more positive reviews, followed by neutral reviews and finally negative reviews. In the training data obtained, accuracy and loss by doing three epochs, namely 20, 50, 100 on the CNN and LSTM algorithms are good enough and not overfitting. Data testing is also carried out using confusion matrix and classification report, based on the two algorithm comparisons, the superior one is using the LSTM algorithm, with accuracy of 92.67% and 93% respectively, while the CNN accuracy results are 84.08% and 84% respectively.

SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY TOWARDS ELECTRIC MOTORCYCLES ON TWITTER USING ORANGE DATA MINING

This study explores sentiment analysis of the Indonesian community towards electric motorcycles on Twitter using Orange Data Mining. In the context of the increasing popularity of electric vehicles, especially electric motorcycles, understanding public sentiment becomes crucial for various stakeholders. Twitter, as a leading social media platform, serves as a rich source of opinions and discussions on various topics, including electric motorcycles. This research utilizes Orange Data Mining with multilingual sentiment analysis techniques to analyze the sentiment of the Indonesian community regarding electric motorcycles. The results of sentiment analysis are visualized through box plots and scatter plots, aiming to classify Twitter users based on their emotional responses. The findings of this study provide valuable insights into the sentiment landscape surrounding electric motorcycles in Indonesia, benefiting policymakers, manufacturers, and marketers in understanding public perception and making informed decisions.

How Attitudes Toward Online Learning Changes During Pandemic, and Reasons Behind It

This paper aim to understand peoples attitude toward online learning and find the reason behind it. The emergence of new coronavirus disease has revolutionized every aspect of people's lives. During the pandemic, all those who try to receive education, students, have to study online. Online learning has become a famous topic during a pandemic. People receive online education and this research is trying to find what are peoples attitude towards this new form of education and does this attitude change during pandemic. At same time, social media are heavily used as a platform for people to express their emotions. In this research, data from posts of Twitter users under the topic of online learning are analyzed using sentiment analysis and MDCOR. The results of sentiment analysis tell us the attitudes change during the pandemic and MDCOR shows potential reason behind it.

Implementasi Algoritma Naive Bayes Multimonial dalam Menganalisa Sentimen Masyarakat di Twitter (Studi Kasus : Perpindahan Ibu Kota)

The move to Indonesia's capital city received different responses from the public, pros and cons. This has an influence on the development of the capital itself. The impact of the move will be felt by the community, especially the people of the Kalimantan area or private developers who want to invest. This research will discuss the classification of public opinions regarding the transfer of the capital which are categorized into 3 categories, namely neutral, negative and positive. The aim of this research is to obtain the results of sentiment analysis or public responses to the move of the nation's capital. The benefit for the government or development is as input regarding the direction of development, the economy and policies regarding the development of the nation's capital city. Data collection was obtained from Twitter using scrapping techniques. The sentiment analysis stage is carried out by preprocessing first, namely by labeling and grouping. At the analysis stage, the Multinomial Naïve Bayes algorithm was used. This method was chosen as an analysis method because this method is able to measure the frequency of occurrence of words in documents more effectively. The classification results obtained from the 1049 data used were that 25.54% stated that public sentiment was neutral, 35.08% stated that public sentiment was negative, and 39.37% stated that public sentiment was positive. The accuracy rate was also obtained at 92.67%.

DL-TBAM: Deep Learning Transformer based Architecture Model for Sentiment Analysis on Tamil-English Dataset

NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models.

Sentiment Analysis - An optimized Weighted Horizontal Ensemble approach

Sentiment Analysis has gained authority as one of the primary means of analyzing feedbacks and opinion by individuals, organizations and governments. The result of sentiment analysis informs an organization on areas to improve and how best to manage customers. While sentiment analysis may be misleading as no algorithm has been considered 100% efficient, the choice of algorithms can optimize the result based on the dataset in question. This paper aims at studying various algorithms and implementing a weighted horizontal ensemble algorithm as a panacea to low confidence level in the results of sentiment analysis. We designed a system that implements the original Naive Bayes algorithm, Multinomial Naïve Bayes algorithm, Bernoulli Native Bayes algorithm, Logistic Regression algorithm, Linear Support Vector Classifier algorithm and the Stochastic Gradient Descent algorithm. Our dataset was sourced from the Stanford University. It contains fifty thousand (50,000) movie reviews. Dataset from the Nigerian movie review was used to test the models. The reviews were encoded as a sequence of word indices. An accuracy of over 91% was achieved. The Ensemble technique delivered an F1-measure of 90%. Ensemble technique provides a more reliable confidence level on sentiment analysis. The researchers also discovered that change in writing style can affect the performance of sentiment analysis

Integrating TextBlob and VADER for Dynamic Sentiment Analysis: A GUI-Based Approach with Emotion Visualization and Confidence Assessment

Abstract: This research presents a Graphical User Interface (GUI) application developed in Python using the Tkinter library for sentiment analysis. The GUI integrates two distinct sentiment analysis libraries, TextBlob and VADER, to provide a comprehensive analysis of text sentiment. Each library's sentiment analysis results are displayed along with confidence scores, allowing users to gauge the reliability of the analysis. Additionally, the GUI features a visual indicator in the form of a lamp that dynamically changes color based on the detected emotion (positive, negative, or neutral) inferred from the sentiment analysis results. Moreover, the GUI includes a combined sentiment score, calculated by averaging the sentiment scores obtained from both TextBlob and VADER, enhancing the overall accuracy and reliability of the sentiment analysis. This GUI provides users with an intuitive and interactive tool for analyzing text sentiment, with the ability to visualize emotions and assess sentiment confidence.

Sentiment analysis of the awareness of environmental sustainability

This study examines the sentiment analysis of awareness of environmental sustainability. Environmental sustainability is the responsible management and utilization of Earth's natural resources to meet the needs of the present generation and ensure that future generations will access those resources. The awareness of environmental sustainability has been growing globally as people, businesses, and governments recognize the importance of preserving the planet for current and future generations. Sentiment analysis of environmental sustainability involves evaluating opinions, attitudes, and emotions expressed in texts related to environmental sustainability, and analyzing sentiment can provide insights into public perception, awareness, and engagement with environmental issues. This exploratory study's primary goal is to conduct social media opinion mining in the context of Thai people's environmental sustainability. The paper presented how to build a model of sentiment analysis with linguistic analysis, including data preprocessing steps, feature extraction, and model constructions. The techniques used in this research include Logistic Regression, Random Forests, Support Vector Machine, Word Segmentation and Bag of Words. The result shows that the model is able to categorize sentiment analysis opinions in the sustainability context primarily in positive terms. The positive sentiments suggest a sustained, long-term shift in awareness, or they might be influenced by specific events or trends. However, positive sentiment analysis results are expressed towards environmental sustainability initiatives, such as renewable energy projects, waste reduction efforts, or conservation programs. Moreover, public awareness plays a crucial role in influencing individual behavior, corporate practices, and government policies towards a more sustainable and environmentally conscious future.

Implementation of Text Mining in Socio-Economic Research

This work aims to analyze insights from social networks for identification of population satisfaction with pay level in Russia using the text mining approach. For this, a sentiment analysis framework was developed, which integrates Twitter mining tools and a sentiment index. Sentiments were extracted using Twitter mining and then recoded and substituted into the sentiment formula. The results of sentiment analysis indicate low satisfaction with levels of pay among Russians. Twitter was chosen as the object of research, as one of the most active and independent networks in Russia. It is possible that some of the tweets belong to authors who are not living in Russia at the moment, but their number is not significant and their interest in this issue, in the authors' opinion, only enhances the relevance of the problem under study.

An exploration system to effectively analyze the visual metaphor used in sentiment visualization

In the field of data visualization, there has been a recent trend of using a complex type of visualization with a multidimensional structure or using several visualizations in parallel when summarizing the results of sentiment analysis. Although this trend may be useful for sophisticated sentiment analysis, such analysis is difficult for the general public and novice researchers. To address this issue, there has recently been a trend of visualizing sentiments using visual metaphors. To facilitate the understanding of related cases, it is necessary to have a systematic means of grasping the sentiment target, the purpose and motivation of research, and the representations used as substitutes for visual metaphors. Therefore, the objective of the present study was to develop an exploration system that can analyze the visual metaphors used in the case of sentiment visualization. For this study, (1) sentiment visualization cases in which visual metaphors are used were collected. (2) After a taxonomy composed of the categories of “target, intermediation, representation, visual variable, and visualization technique” was constructed, it was used to analyze sentences of visual metaphors appearing in sentiment visualization cases. (3) An exploration system capable of grasping the semantic relationships of sub-elements within the five categories of the taxonomy and intuitively interpreting visual metaphors was developed so that appropriate cases can be recommended to sentiment visualization researchers. (4) The approach and usefulness of the exploration system were explained using user scenarios. (5) A case study was conducted to show that the provided system can be analyzed from various perspectives. (6) The usability of the exploration system was demonstrated through a verification targeting experts. The proposed system allows researchers and analysts to intuitively grasp “what types of visual metaphor method and idea should be equipped to visualize sentiment data in an easier way to understand.”

Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language Analysis

The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas—Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories Regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative. For the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative. For the videos belonging to the theme of Conspiracy Theories Regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative. For the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were neutral. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions for each topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels, such as Facebook and Twitter, were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of tweets and the correlation between the length of the video title and the number of Facebook posts were statistically significant.

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.