Results Of Sentiment Analysis Research Articles

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.

SENTIMENT ANALYSIS OF REVIEW DATA OF THE RUANGGURU ONLINE LEARNING APPLICATION USING THE C5.0 ALGORITHM

Sentiment analysis is process to determine the sentiment of a person that is manifested in the form of text. Internet users write their opinions and everything that concerns them in the google play store review column. Moreover, when the world of education could not carry out face-to-face learning due to the covid-19 pandemic, learning turned to e-learning applications. Through this innovation, many pros and cons flow from the community with the existence of Ruangguru online learning application in the world of education. This research was conducted with the aim of determining word cloud visualization and the accuracy of the results of sentiment analysis of review data on the Ruangguru application using the C5.0 algorithm. The word cloud visualization results are dominated by word such as “paham”, “bagus”, “mudah”, “suka”, “langganan”, “seru”, “nyaman”, “senang”, “menarik”, “keren”, “lancar”, “sukses”. This shows that Ruangguru Application is a good application because it is dominated by positive sentiment words which means that users find it helpful and easy to understand the learning material in Ruangguru. The results of the Confusion Matrix show that the model successfully classifies 0.8557 or 85.57% of the data correctly from all test data

Mental Health Prediction using Sentimental Analysis

Abstract: The objective of this paper is to create a mental health prediction system through text sentimental analysis. The system will employ natural language processing technique to examine expressions and sentiments conveyed in text, aiming to predict the likelihood of an individual experiencing mental health issues such as anxiety, depression, or stress. The proposed system will make use of machine learning models to extract features that will provide with the results of sentiment analysis derived from text input. The integrated features will then be utilized to predict the likelihood of an individual experiencing mental health issues.

Exploring Sentiment Extraction from Dota 2 Update 7.33 “The New Frontiers” Tweets Using STANZA and NLTK

Updates of a game can give a different experience while playing and sometimes it becomes the most awaited and decisive point for players who have left the game to return. Updates can come in the form of gameplay changes or character skin updates. Dota 2 game has complex game elements ranging from choosing heroes to choosing the right items to counter the opponent's hero. The big updates provided by the Dota 2 game are always awaited by casual to professional players and also enthusiasts of the game, some update may lead to negative impact in gameplay and otherwise. In this study, a classification will be carried out from the results of sentiment analysis given by people on the biggest Dota 2 ver 3.33 update from twitter. This update overhauls many elements in the game and to find out whether there are many people who like it or not, the test was conducted. From the results of the sentiment analysis, a comparison of the automatic targeting using the Stanza and NLTK libraries is also performed, which results in a different number of positive, neutral and negative statement values.

Sarcasm Detection in News Headline Dataset with Ensemble Deep Learning Method

Sarcasm, a prevalent linguistic device, is frequently used in public discourse, often causing offence and distress to the listener. The complexity inherent in detecting sarcasm is a significant and ongoing challenge in the field of sentiment analysis research. The widespread use of this phenomenon in diverse conversational contexts further complicates its identification in data sets full of human interactions. Deficiencies in methodologies for distinguishing such statements adversely affect the performance of sentiment analysis, especially in distinguishing negative, positive or neutral sentiments. Inaccuracies in sarcasm detection can affect the classification results of sentiment analysis. Therefore, sentiment analysis seeks to categorise sarcastic sentences that, despite appearing positive, actually contain negative meanings. This research aims to build a deep learning ensemble stack model. The basic deep learning methods used are Bidirectional Gated Recurrent Unit (BiGRU) and Convolutional Neural Network (CNN). LightGBM is used to perform stack ensemble of deep learning methods. The dataset used comes from the Kaggle website and consists of English headlines. The findings show that the stack ensemble method outperforms BiGRU and CNN, evidenced by an accuracy rate of 91.2% and an F1 score of 90.2%. Therefore, from the above discussion, it can be concluded that the LightGBM method emerges as the optimal solution for sarcasm detection

Mining and fusing unstructured online reviews and structured public index data for hospital selection

In the era of big data, publicly available data from official hospital sources and data from online reviews are easy to influence a patient's decision in choosing a hospital. However, existing research has rarely comprehensively considered the combined influence of these two factors. This paper proposes a hospital selection approach based on a fuzzy multi-criterion decision-making method, which considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. First, online reviews of general and specialized hospitals are collected and processed to get evaluation attributes of hospital and attribute weights. Then, text reviews are taken to classify topics and sentiments using logistic regression, light gradient boosting machine, and bidirectional encoder representations from transformers. The results of sentiment analysis are quantified using triangular fuzzy numbers to express evaluation values of hospitals. Based on patients’ preferences for online reviews and structured data on publicly available attributes of hospitals, final preference scores of hospitals are obtained using the fuzzy technique for order preference by similarity to ideal solution method. A case study is performed to illustrate the applicability of the proposed method. Robustness analysis from patient perspective and hospital perspective are executed to validate the effectiveness of the method.

Evaluation on ChatGPT for Chinese Language Understanding

ABSTRACT ChatGPT has attracted extension attention of academia and industry. This paper aims to evaluate ChatGPT in Chinese language understanding capability on 6 tasks using 11 datasets. Experiments indicate that ChatGPT achieved competitive results in sentiment analysis, summary, and reading comprehension in Chinese, while it is prone to factual errors in closed-book QA. Further, on two more difficult Chinese understanding tasks, that is, idiom fill-in-the-blank and cants understanding, we found that a simple chain-of-thought prompt can improve the accuracy of ChatGPT in complex reasoning. This paper further analyses the possible risks of using ChatGPT based on the results. Finally, we briefly describe the research and development progress of our ChatBIT.

Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis

This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these Tweets were positive. The results of gender-specific sentiment analysis indicate that for positive Tweets, negative Tweets, and neutral Tweets, between males and females, males posted a higher percentage of the Tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated Tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that females posted a higher percentage of highly opinionated Tweets as compared to males. However, males posted a higher percentage of least opinionated and neutral opinionated Tweets as compared to females. Third, toxicity detection was performed on the Tweets to detect different categories of toxic content—toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of Tweets posted by each gender for each of these categories of toxic content revealed several novel insights related to the degree, type, variations, and trends of toxic content posted by males and females related to online learning. Fourth, the average activity of males and females per month in this context was calculated. The findings indicate that the average activity of females was higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India, a higher percentage of the Tweets about online learning during COVID-19 were posted by males as compared to females.

Analisis Sentimen Terhadap Bakal Capres RI 2024 di Twitter Menggunakan Algoritma SVM

In 2024, Indonesia will hold an election for head of state which will be the pinnacle of democracy in its political system. In the context of democracy, active community participation plays a key role in making general elections successful. However, there is still a tendency for people to only follow election procedures without actively participating. Therefore, this research was conducted to analyze public sentiment towards the 2024 presidential candidates, using data from social media Twitter. Social media, especially Twitter, has become the main platform for Indonesian people to express their political opinions, views and preferences. With the number of social media users reaching 170 million in 2021, Indonesia has great potential to gather various opinions and sentiments regarding general elections. This research aims to analyze sentiment using the Support Vector Machine (SVM) method on tweets containing hashtags related to three presidential candidates, namely Aniesbaswedan, Ganjarpranowo, and Prabowosubianto. The results of sentiment analysis show that the SVM method can be used to analyze sentiment with an accuracy of around 78.3 % overall. This accuracy is influenced by factors such as the amount of data used and the composition of positive and negative data. The sentiment data analyzed came from 1719 tweets from Twitter users, with a distribution of around 597 data for Aniesbaswedan, 627 data for Ganjarpranowo, and 495 data for Prabowo Subianto. This research provides insight into how Indonesian people convey their sentiments regarding the 2024 presidential candidates via social media. . This sentiment analysis can be an important reference in understanding the public's political preferences ahead of the upcoming general election.