Real-time Lexicon-based sentiment analysis experiments on Twitter with a mild (more information, less data) approach

The classification is the technique which can classify the data into certain number of classes according to input data. The sentiment analysis is the terms which is used to analyze sentiments of the input data. In this research work, sentiment analysis of twitter data is performed using the technique of classification. In the previous work, Support Vector Machine (SVM) classifier is applied for the sentiment analysis which later replaced with K-Nearest Neighbor (KNN) classifier to increase accuracy of classification. In this work, ontology based technique is proposed to generate summary of the input data. The existing and proposed techniques are implemented in python and results are analyzed in terms of accuracy.

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.

Chapter 7 - The COVID-19 outbreak: social media sentiment analysis of public reactions with a multidimensional perspective

Social media along with different web forums contribute to a huge amount of data, i.e. opinions, feedbacks, reviews generated on a daily basis. Sentiment analysis is the identification of polarity (positive, negative or neutral) of the data to analyse the opinion of users on various platforms on a range of topics including those that have real business value, i.e. polarity of users on a product. With the advent of platforms like Twitter, people freely express their opinion on almost everything and that generates a huge amount of data which cannot be processed or analysed manually; therefore, we have various NLP and machine learning techniques which can effectively analyse and predict the polarity of the data which enables us to capture the sentiment of the people regarding a particular issue. In this paper, we intend to analyse the different machine learning algorithms for sentiment analysis of Twitter data and compare the algorithms’ performance on different datasets with the help of metrics like precision, accuracy, F-measure and recall.

Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers

Sentiment analysis of Twitter data is an area that has experienced significant growth in recent years. The ability to identify sentiment from tweets using machine learning techniques has attracted researchers because of the simple efficiency of machine learning techniques. This paper tackles the use of machine learning algorithms and Scikit-learn in sentiment analysis of Twitter data. To do this, we perform analyses on Twitter datasets made publicly available by NLTK Corpora and create an efficient feature by using a feature extraction technique. We train and test various machine learning classifiers such as MultinomialNB, BernoulliNB, LogisticRegression, SGD classifier, SVC, LinearSVC, and NuSVC. Experimental results demonstrate that BernoulliNB, LogisticRegression, and SGD classifier reached accuracy as high as 75%.

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.

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating disparities, and addressing persistent barriers to care. Since Twitter data has shown significant promise as a representation of human behavior and for opinion mining, this study leverages Twitter data published in 2020 to characterize the pandemic-related experiences of the United States’ African American population using aspect-based sentiment analysis. Sentiment analysis is a common task in natural language processing that identifies the emotional tone (i.e., positive, negative, or neutral) of a text sample. Aspect-based sentiment analysis increases the granularity of sentiment analysis by also extracting the aspect for which sentiment is expressed. We developed a machine learning pipeline consisting of image and language-based classification models to filter out tweets not related to COVID-19 and those unlikely published by African American Twitter subscribers, leading to an analysis of nearly 4 million tweets. Overall, our results show that the majority of tweets had a negative tone, and that the days with larger numbers of published tweets often coincided with major U.S. events related to the pandemic as suggested by major news headlines (e.g., vaccine rollout). We also show how word usage evolved throughout the year (e.g., outbreak to pandemic and coronavirus to covid). This work also points to important issues like food insecurity and vaccine hesitation, along with exposing semantic relationships between words, such as covid and exhausted. As such, this work furthers understanding of how the nationwide progression of the pandemic may have impacted the narratives of African American Twitter users.

Purpose. Microblogging sites are being used by people across the globe to share their opinions and to express sentiments for everything in real time. Through social listening, companies analyse the sentiments to assess the way forward, and the researchers use it to analyse the trend or an event and give forward-looking recommendations. The objective of the paper is to analyse the sentiments of people relating to Paytm IPO which can be used to predict the way forward. Design/methodology/approach. The study attempts sentiment analysis. For this purpose, QSR NVIVO 12, the qualitative analysis tool was used to retrieve the tweets from the Twitter website. NCapture was installed for this purpose. Post data cleaning, stemming, query augmentation and classification, the Twitter data was analysed. Findings. The sentiments around the IPO of Paytm have been negative and sarcastic. The extremely negative tweets were near twice the number of extremely positive tweets. Practical implication. The study can help an investor in evaluating the investment that they might be planning in the given company. For the company, whose IPO is being considered, an analysis of the sentiments around the IPO can help in taking corrective measures, if the sentiment is negative, towards reputation building. Originality/value. The study is an original contribution to the extant literature in the field of sentiment analysis.

Social media platforms, particularly Twitter, are frequently employed by individuals to articulate their opinions on various subjects in textual form. The proliferation of viewpoints from diverse sources can influence public perceptions on these topics. The greater the popularity of a topic, the more abundant the opinions generated. Currently, the most widely discussed topic is the 2024 Indonesian presidential election. Sentiment analysis, or opinion mining, is an academic discipline that examines sentiments towards a given entity, while text mining involves the extraction of information through processing, classifying, and analyzing extensive datasets. This study will utilize data crawling techniques to gather data from Twitter which will subsequently undergo preprocessing and cleaning. Following this, the cleaned data will be classified by sentiment (positive, negative, or neutral) using a pre-trained language model (BERT) and Natural Language Toolkit (NLTK). The classified data will then be visualized with tools such as Matplotlib and Wordcloud to elucidate the data distribution.

Sentiment analysis of the text data available on the web either in the form of blogs or at social media sites such as Twitter, Facebook, and LinkedIn, offers information through which to assess people's perspective of products and services that are of interest to them. Consumers routinely scour the Internet to assess other user's reviews for a product or a service before making their own decision. Likewise, these same data can potentially provide businesses a snapshot in time of the users' response to their products/services and even the trends over time. This information gained through sentiment analysis can then be used by businesses to make decisions about improving their products and services, and gain an increased edge over their competitors. In this paper, we illustrate the potential of sentiment analysis of Twitter data to gauge users' response to popular smart phone brands and their underlying operating systems. Specifically, our objective is to investigate whether the tweets available on the web are sufficient to gain useful insight about the performance of popular smart phone brands, their battery life, screen quality, and on the perceived performance of the phones operating systems. Our results show that although the Twitter data does provide some information about users' sentiments to the popular smart phone brands and their underlying operating systems, the amount of data available for different brands varies significantly. This limitation makes the comprehensive analysis of users' response somewhat more challenging for some brands compared to others and consequently makes the comparison between brands almost impossible.

Sentiment Analysis is a process of computing, identifying, and categorizing opinion from a piece of text and discovering if the writer’s attitude toward a particular topic or product is positive or negative. In this paper, we are doing Sentiment Analysis of Twitter Data which are mixed code (English and Hindi). Our model works on different types of Twitter input data, namely Mixed script—the tweet is written in combination of Native script and Roman script, Code-Mixed script—in this type, the tweet is in native language but Roman script was used to write it, and Native script—here, the Regional language tweet is written in Native script. We are developing our model in Jupyter Notebook which is an open-source web application software. Our main aim is to increase the efficiency of the Machine Learning model, so we are building the machine learning model with pipeline techniques and Hyperparameter Tuning technique. We are using the Twitter Training Dataset to train our model. Initial step is to do the Text pre-processing thereby removing the unwanted noise in the data. We are using the cross-validation method to overcome the underfitting and overfitting problems. We build the model with different classifiers, namely Logistic Regression, Decision Tree, Random forest, Multinomial Naive Bayes, Support Vector and vectorizers, namely count and term frequency-inverse document frequency (TFIDF). We tried the different combination of parameter tuning and found the best parameter which gives the maximum accuracy. After the analysis and voting classifier output, we found that the combination of TFIDF vectorizer and Random Forest Classifier pipeline gives the maximum accuracy of 96.57% and Predicts the tweets in real time as Positive or Negative.

Twitter is the widely used microblogging site, where millions of people share their feelings, views, or opinion regarding different things be it a product, service, or events. Huge volumes of data are being produced hourly because of the increase in the number of users. These data are unstructured in nature, and thus, it is a difficult task to analyze them and extract the meaning from it. This paper, however, will concentrate on sentiment analysis of Twitter data. We will perform text mining or opinion mining to obtain a better understanding of public sentiment. In this paper, Python is used to acquire, preprocess, and analyze tweets, after those three different machine learning algorithms and one deep learning algorithm are used for sentiment analysis and comparison is done among them to determine which approach or model gives the best accuracy.KeywordsTwitterSentiment analysisMachine learningDeep learningPython

Amid the ever-growing social media content, sentiment analysis is a technique that is necessary for analyzing public opinion. This paper aims to discover what sentiment prevailed and what reaction took place preceding the election. This paper also presents the results of a study that applied sentiment analysis to Twitter data relating to the 15th General Elections of Malaysia. This research reviews several sentiment analysis techniques based on Twitter data. Using the method of analyzing 1566 tweets, including re-tweets and replies, gathered between November 12th and November 18th, 2022, the findings give us an understanding of the level of emotions that were tweeted by the users of Twitter towards the 15th general election in Malaysia. The results indicate that the sentiments expressed in the analysed tweets are distributed as follows: slightly positive (41%), positive (31%), neutral (24%), and slightly negative (4%).

IntroductionIn recent years, with the rapid advancement of urbanization in China and the successive implementation of major national strategies such as the Belt and Road Initiative, the Sichuan–Xizang Railway, and the South-to-North Water Diversion Project, the potential risks and losses from geological disasters have continued to rise. Secondary disasters—including landslides, mudslides, and barrier lakes triggered by earthquakes—have significantly intensified the overall impact, posing severe challenges to disaster monitoring, early warning, emergency response, recovery, and reconstruction efforts. In this context, how to leverage new information technologies to achieve in-depth mining and application of geological disaster data has become a critical issue in disaster risk reduction and sustainable crisis management.MethodsThis study focuses on topic modeling and sentiment analysis of disaster-related data, using geological disasters in China as a background. First, it reviews the recent advances in topic modeling and sentiment analysis techniques. Then, based on data characteristics and applicability, two major social media platforms—Weibo (Sina Weibo) and Rednote (Xiaohongshu)—are selected as primary data sources. The advantages of the LDA topic model (e.g., its unlabeled and multi-topic capabilities) and the lightweight processing efficiency of the SnowNLP sentiment analysis algorithm are discussed. As a case study, the “1•07″earthquake in Xigaze, Tibet, in 2025 is analyzed. The LDA model is used to conduct multi-topic classification and clustering visualization of Weibo disaster topic data. Combined with the SnowNLP sentiment analysis algorithm, the phased sentiment evolution judgment application is carried out using the 6-month Rednote comment data.ResultsThe LDA model effectively extracts geological disaster-related themes—such as emergency response and post-disaster recovery—and that sentiment analysis technology can reveal phase-based patterns in public emotions.ConclusionThese findings provide scientific support for geological disaster emergency management and public opinion guidance. The research also expands the application potential of topic modeling and sentiment analysis in the field of geological disasters and offers a direction for future integration and optimization of multimodal social media data.