Novel cluster set optimization model with unique identifier tagging for twitter data analysis

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A few studies related to the Indonesian election have tried to predict candidates who will run for the presidential election, but there has been no research that examines public sentiment on social media towards each of the potential candidates. The main objective of this study is to analyze the public sentiment in Twitter towards potential candidates for the 2024 Indonesian presidential election. This research seeks to fill the gaps in previous research and become a reference for further research regarding sentiment analysis for election prediction using Twitter. The presidential candidates used in the research are the top 3 candidates based on the Poltracking survey, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan. The data were taken from January until October 2022, more than a year before the general election began. To predict the sentiment, four different machine-learning methods were used and compared to each other. There are Naïve Bayes, Support Vector Machines, Random Forests, and Neural Networks. Based on the sentiment results of each candidate, the highest sentiment towards Prabowo is neutral (55.49%), the highest sentiment towards Ganjar is positive (61.34%), and the highest sentiment towards Anies is neutral (44.84%). Results from the study also show that Anies was the presidential candidate who received more negative sentiment than the other two (56.63%). Meanwhile, Ganjar Pranowo got the most positive sentiment of all (42,69%). For the neutral sentiment, Anies Baswedan also got the most results (39,87%), followed by Prabowo (38.99%) and Ganjar Pranowo (21.14%). The result of the study also discovered that random forest and neural networks have the best performance for sentiment analysis.

This paper presents the performance of Twitter sentiment analysis on the Agricultural Ministry of India and also determines the optimal AI model between random forest (RF) and k nearest neighbours (KNN) algorithms. ‘Twitter and Reddit Sentimental Analysis’ dataset from Kaggle is used to train and test the RF and KNN AI models. Term frequency-inverse document frequency (TF-IDF) vectorizer is used to extract features from the obtained dataset. The optimal AI model is determined using the library functions of scikit-learn-2.0 which perform the training and testing of the dataset. Twitter authenticated API called Tweepy is used to create the dataset required to determine the public sentiment towards the Agriculture Ministry by us. This dataset includes 6000 tweets related to the Agriculture Ministry. The Twitter sentiment analysis performed determines what percentage of public opinion towards the Agriculture Ministry is positive, negative and neutral.KeywordsTwitter sentiment analysisRandom forest algorithmK nearest neighbours algorithmTweepyAgricultureTweets

This study aims to analyze the sentiment of the Hindu community toward religious issues on social media, with a specific focus on Kapuas Regency, Central Kalimantan. The development of social media as a digital public sphere has positioned platforms such as Instagram and Twitter as primary spaces for the spontaneous and open expression of opinions, perceptions, and religious attitudes. This study is important because religious issues in digital spaces often influence social harmony and shape religious discourse within society. The research employs a quantitative approach using text mining and sentiment analysis based on Natural Language Processing (NLP). Primary data were collected through web scraping techniques, utilizing Apify for Instagram and asynchronous Python scripts for Twitter, with relevant keywords, hashtags, and geographic indicators. The analysis process includes text preprocessing (cleaning, tokenization, stopword removal, and stemming), followed by sentiment classification using a lexicon-based approach with the InSet dictionary into three categories: positive, negative, and neutral. The analysis results were evaluated using a confusion matrix, along with precision, recall, and F1-score metrics to assess model reliability. The findings indicate that positive sentiment predominates on both Instagram and Twitter, followed by neutral sentiment, while negative sentiment appears in only a small proportion. Positive sentiment is generally associated with expressions of prayer, gratitude, tolerance, and calls for togetherness, whereas negative sentiment tends to emerge in discussions related to ritual differences or responses to socio-religious controversies. The sentiment analysis model achieved an accuracy of 100% on Instagram data (self-evaluation) and 74.4% on Twitter data (manual evaluation), with relatively high precision and recall values, indicating that the results are statistically reliable.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords – Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution

Social media has become an essential part of everyday communication, including within the library context. Twitter, in particular, provides an Application Programming Interface (API) that enables real-time and comprehensive text-mining analyses of users’ perceptions and experiences. This study examines efforts to enhance library service quality by identifying and interpreting user sentiments on Twitter in a more detailed manner, enabling libraries to formulate targeted and effective service improvement strategies. The research consists of several stages: a literature review on sentiment analysis, data collection from Twitter using API-based data crawling, and a series of pre-processing steps, including data cleaning, case folding, tokenization, numerization, stopword removal, and stemming. Data were analyzed using the Aspect-Based Sentiment Analysis (ABSA) method. The findings indicate that the Collection and Facilities aspects receive the highest levels of positive sentiment, while Accessibility and Service attract considerable negative sentiment, signaling priority areas for improvement. Based on these results, libraries may consider extending operational hours, improving digital access, updating collections, and renovating facilities. Additionally, strengthening staff competencies and interpersonal skills is crucial for improving service quality and increasing user satisfaction.

The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods.

Twitter is attracting significant interests from the research community in the last few years. Sentiment analysis of tweets is among the hottest topics of research nowadays. State of the art approaches of sentiment analysis present many shortcomings when classifying tweets, in particular when the classification goes beyond the binary or ternary classification. Multi-class sentiment analysis has proven to be a very challenging task. This is mainly for the simple reason that a tweet usually does not contain a single sentiment, but many ones. In this paper, we propose a pattern-based approach for sentiment quantification in Twitter. By quantification, we refer to the detection of the existing sentiments within a tweet and the detection of the weight of these sentiments. In a first step, we classify tweets into positive, negative, or neutral. Our approach reaches an accuracy of 81%. We then perform the sentiment quantification on the sentimental tweets (i.e., positive and negative ones) to extract the sentiments within them: we define 5 positive sentiment sub-classes 5 negative ones and detect which exist in each tweet. We define 2 metrics to measure the correctness of sentiment detection, and prove that sentiment quantification can be a more meaningful task than the regular multi-class classification.

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.

The literature scrutinizes on diverse techniques that are associated with sentiment analysis in twitter data. It reviews several research papers and states the significant analysis. Initially, the analysis depicts various schemes that are contributed in different papers. Subsequently, the analysis also focuses on various features and it also analyses the sentiment analysis in twitter data that is exploited in each paper. Furthermore, this paper provides the detailed study regarding the performance measures and maximum performance achievements in each contribution. Finally, it extends the various research issues which can be useful for the researchers to accomplish further research on sentiment analysis in twitter data.

Public and private opinions on a wide range of topics are expressed and regularly disseminated through several social media channels. One of the social media platforms that is growing in popularity is Twitter. The emotional impact of a person plays an important role in daily life. The method of assessing a person's opinions and thought polarity is known as sentiment analysis. Here, this study addresses the sentiment classification problem on the Twitter dataset. A number of text preprocessing methods and Naive Bayes (NB) classifiers is used to perform sentiment analysis in the proposed system. Preprocessing procedures typically involve eliminating stop words, changing the case of the words to make them more normal, and using stemming or lemmatization. Twitter Sentiment Analysis is a method used for analyzing emotions from tweets. Tweets are useful in obtaining sentiment values from a user. The data provides an indication of polarity as positive, negative or unbiased values.

This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.

Twitter serves as a dynamic platform for real-time communication, where users share opinions, news, and trends globally. The vast volume of daily tweets provides a rich source for sentiment analysis, enabling organizations to effectively gauge public opinion and consumer sentiment. This data-driven analysis not only supports marketing strategies but also enhances decision-making in areas like politics, public health, and brand management. However, several studies face limitations such as low data quality, contextual challenges, and resource intensive. To address these issues, this study evaluates the performance of three models, Bert, RoBERTa, and DistilBERT- on Twitter sentiment analysis. The models are assessed based on accuracy, precision, recall, and F1-score. Among the models, RoBERTa demonstrates the highest performance for both positive and negative sentiment analysis.

Opinion mining and sentiment analysis are useful to extract subjective information out of bulk text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. Though performing sentiment analysis is a challenging task for the researchers to identify the user’s sentiments from the large datasets, it is unstructured in nature, and also includes slangs, misspells, and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; they are data collection, pre-processing, keyword extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, pre-processing was carried out for enhancing the quality of collected data. The pre-processing phase comprises of three systems: lemmatization, review spam detection, and removal of stop words and URLs. Then, an effective topic modelling approach latent Dirichlet allocation along with modified possibilistic fuzzy C-Means was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative, and neutral) by applying an effective machine learning classifier: Selective memory architecture-based convolutional neural network. The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6–20% related to the existing systems.