Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed a model for predicting stock movement utilizing SA on Twitter and StockTwits data. Stock movement and sentiment data were used to evaluate this approach and validate it on Microsoft stock. We gathered tweets from Twitter and StockTwits, as well as financial data from Finance Yahoo. SA was applied to tweets, and seven ML classification models were implemented: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron (MLP). The main novelty of this work is that it integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media (i.e., public sentiment), for improving stock prediction accuracy. The best results were obtained when tweets were analyzed using Valence Aware Dictionary and sEntiment Reasoner (VADER) and SVM. The top F-score was 76.3%, while the top Area Under Curve (AUC) value was 67%.

There are several factors that affect the box office performance of a movie. The success of a movie may depend on the star cast, acting, direction, promotion, and marketing. This study presents a method based on an enhanced VADER (Valence Aware Dictionary and sEntiment Reasoner) model to predict the box office performance by analysing the sentiment of movie related news headlines. VADER can effectively analyse the sentiment of a general text, but it cannot capture domain-specific sentiments. It also fails to capture the temporal trends in the data. To overcome these limitations, we introduce three major enhancements: (1) a temporal sentiment profiling technique that can capture the changes in sentiments over time (2) a weighted sentiment based on closeness to the movie's release (3) adding relevant words to VADER's lexicon that appear in movie headlines. These values are then used to train various Machine Learning models to predict the box office performance. A comparison of VADER-only and enhanced VADER-based approach of around 5000 headlines and box office revenues shows that the enhanced model performs better than the baseline VADER-only approach. Among all Machine Learning models, RF regression achieved the best R 2 score of .77.

IntroductionAlthough global food production has tripled, sustainable agriculture remains threatened by climate change, land degradation, and financial gaps, especially in smallholder communities. This study introduces the Integrated Waqf Environment Model (IWEM) as a waqf-based funding mechanism to support sustainable agriculture. Additionally, sentiment analysis using VADER Python is employed to assess public perceptions of sustainable agriculture.ObjectivesThis research aims to measure public sentiment regarding sustainable agriculture on the Twitter social media platform. Additionally, the study proposes the Integrated Waqf Environment Model (IWEM) for sustainable agriculture funding as a response to the challenges faced in the agricultural sector.MethodThe research methodology involves the extraction and classification of tweet data using the Python library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The study utilizes tweet data posted within the last year. The research findings indicate fluctuations and a decrease in the number of tweets discussing sustainable agriculture. The location with the highest tweet activity related to sustainable agriculture is Brussels, Belgium, with 642 tweets during the observation period.ResultsWord cloud analysis on keywords reveals that, in positive sentiment, terms like "food security" and "climate change" dominate the visualization. On the other hand, in negative sentiment, words like "farmer" and "private farmland" appear more frequently. Overall, the majority of tweets express a positive attitude toward sustainable agriculture, with 68.5% positive sentiment. Approximately 22.3% of tweets indicate neutral sentiment, without a strong positive or negative tendency. Only 9.1% of tweets contain negative sentiment, suggesting that a small portion expresses less supportive views of sustainable agriculture.ImplicationsThe research provides insights into public sentiment on sustainable agriculture and proposes IWEM as a sustainable financing solution. IWEM can bridge financial gaps, enhance technological adoption, and support policy development for sustainable agriculture. This model aligns with the SDGs, ensuring long-term agricultural resilience and environmental conservation.Originality/NoveltyThis study uniquely integrates Islamic social finance with Smart Agriculture 5.0 through IWEM. It also employs sentiment analysis to understand public perceptions, offering data-driven insights for policymakers. The research contributes a novel waqf-based framework that can be scaled for sustainable agricultural development.

Abstract—This paper presents a hybrid sentiment analysis system that integrates lexicon-based, traditional machine learning, and deep learning techniques to classify textual data into positive, negative, or neutral sentiments. The system leverages Python-based libraries such as Scikit-learn, NLTK, and Transformers to pre- process text, extract features, and apply models in- cluding Support Vector Machine (SVM), Bidirectional Encoder Representations from Transformers (BERT), Linear Regression, and Valence Aware Dictionary and sEntiment Reasoner (VADER). The framework pro- cesses customer reviews from e-commerce platforms (Amazon, Flipkart) and social media (Instagram) using web scraping and provides actionable insights through sentiment summarization and visualizations (bar and pie charts). Experimental results demonstrate BERT’s superior performance with 92.3% accuracy, followed by SVM (85.6%), Linear Regression (81.2%), and VADER (76.8%). The system addresses challenges like sarcasm, class imbalance, and scalability, offering a scalable, user-friendly solution for real-world applications in e- commerce, social media analytics, and brand reputation management. Index Terms—Sentiment Analysis, Machine Learning, Deep Learning, Natural Language Processing, BERT, SVM, VADER, Text Classification, Data Visualization.

To analyze public perceptions of over-the-counter (OTC) hearing aids through sentiment and thematic analysis of online consumer reviews and their changes over time. Sentiment and thematic analysis. Online reviews from third-party and product websites. All English online consumer reviews posted between 2016 and 2024 for OTC hearing aids (83 models) were recorded (n = 21,727). Sentiment analysis was performed using Valence Aware Dictionary and Sentiment Reasoner (VADER), a rule-based sentiment analysis tool incorporating natural language processing. VADER provides scores for each review ranging from -1 (most negative), 0 (neutral), to 1 (most positive). Additional thematic analysis was performed for the top 100 most positive, neutral, and negative reviews (n = 300). Overall, mean (SD) VADER sentiment score of online reviews was generally positive at 0.587 (0.411). Multivariable regression analysis showed that higher VADER scores were associated with higher-priced and behind-the-ear (BTE) type hearing aids. Although there was a significant increase in a number of reviews after the Food and Drug Administration's new establishment of the OTC hearing aid category in 2022, the mean sentiment scores slightly decreased (β =-.10, [95%CI: -0.12 to -0.09]). Thematic analysis revealed that positive sentiments highlighted the affordability and time-saving benefits of OTC hearing aids as alternatives to prescription models. Negative sentiments centered on sound quality, challenges with customer service, and inadequate amplification for those with severe hearing loss. Customers generally viewed OTC hearing aids positively, while mixed experiences were present. When used as indicated for adults with mild to moderate hearing loss, OTC hearing aids may offer a viable alternative to prescription devices, improving accessibility and affordability.

BackgroundData from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing “street names” of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, “brand name” references were more amenable to machine-driven categorization.ObjectiveThis study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment.MethodsWe used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet’s engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency–inverse document frequency score.ResultsUsing BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora.ConclusionsBERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non–drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

Intellectual Property Rights (IPRs) provide a systematic medium to safeguard people's unique ideas. Various authors from all around the globe have contributed to the IPRs in the Law domain by publishing their research articles. Although the literature on the IPRs in the Law domain is found to be several decades old, no Sentimental Analysis has been conducted on it. Identifying this significant research gap, the authors of this research paper have evaluated the IPRs using modern Sentimental Analysis methods in the Law domain. The authors have used Sentimental Analysis methods of Microsoft Azure, Valence Aware Dictionary and Sentiment Reasoner (VADER), and International Business Machines (IBM) Watson to perform this Sentimental Analysis on over six thousand research articles from the past four decades, in which authors from over fifty countries have contributed to the IPR in the Law domain. The authors used significant research paper components, including the Title, Keywords, and overall Contribution of the authors, as input for conducting this Sentimental Analysis. The overall results obtained from Sentimental Analysis methods of Microsoft Azure, VADER, and IBM Watson convey that 83.25 % positive, 13.11 % neutral, and 3.64 % negative research was conducted in the IPRs research in the Law domain. These results convey that, to date, the research in the IPRs of the law domain has been going in a positive direction, thereby providing a solution-oriented positive approach for the upcoming researchers in the IPRs of the law domain.

Prediction of stock prices has been the primary objective of an investor. Any future decision taken by the investor directly depends on the stock prices associated with a company. This work presents a hybrid approach for the prediction of intra-day stock prices by considering both time-series and sentiment analysis. Furthermore, it focuses on long short-term memory (LSTM) architecture for the time-series analysis of stock prices and Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis. LSTM is a modified recurrent neural network (RNN) architecture. It is efficient at extracting patterns over sequential time-series data, where the data spans over long sequences and also overcomes the gradient vanishing problem of RNN. VADER is a lexicon and rule-based sentiment analysis tool attuned to sentiments expressed in social media and news articles. The results of both techniques are combined to forecast the intra-day stock movement and hence the model named as LSTM-VDR. The model is first of its kind, a combination of LSTM and VADER to predict stock prices. The dataset contains closing prices of the stock and recent news articles combined from various online sources. This approach, when applied on the stock prices of Bombay Stock Exchange (BSE) listed companies, has shown improvements in comparison to prior studies.

As internet memes increasingly dominate political communication, concerns about their role in reinforcing biases, fostering polarization, and oversimplifying complex political issues have intensified. Employing Natural Language Processing (NLP) in the context of Sentiment Analysis, the Darth Sentiment Analyzer—a dedicated web-based software based on Valence Aware Dictionary and sEntiment Reasoner (VADER) —was utilized to examine the sentiments (positive, negative, neutral) expressed in a dataset of over 20,000 memes, revealing that neutral sentiment is the most prevalent, followed by positive and negative tones. The study applies Halliday’s Systemic Functional Linguistics and Kress and Van Leeuwen’s Grammar of Visual Design to analyze both the textual and visual elements of memes, providing deeper insights into how these components collaboratively influence public sentiment. Key findings demonstrate significant variation in sentiment across political affiliations, with liberalism being the most prominent ideology, while fascism and green ideologies are underrepresented. The study also identifies the ideological appeals that shape political engagement in online spaces, with implications for educational practices. The research contributes to the growing body of research on the intersection of technology, politics, and education, emphasizing the need for responsible digital citizenship in an era of widespread online influence. KEYWORDS: Education, Applied linguistics, Internet memes, natural language processing, sentiment analysis, Filipino voters, Philippines

This study explores public sentiment toward the Merdeka Curriculum, a policy reform introduced to address educational challenges in Indonesia following the COVID-19 pandemic. Utilizing Twitter (now X) as a data source, this research collected 1,900 Indonesian-language tweets to assess how society perceives the curriculum’s flexibility and relevance. The analysis begins with data preprocessing, including cleaning, tokenization, stopword removal, and stemming. VADER (Valence Aware Dictionary and Sentiment Reasoner) is employed for initial sentiment labeling, classifying tweets into positive, neutral, and negative categories. The results reveal a significant class imbalance: 1,692 neutral, 117 positive, and 91 negative tweets. These labeled tweets are further analyzed using the Long Short-Term Memory (LSTM) algorithm for deep sentiment classification. The LSTM model demonstrates training accuracy improvements from 82% to 90.29% across five epochs, while validation accuracy remains steady at 88%. However, the model fails to accurately classify minority classes, with precision, recall, and F1-scores for positive and negative sentiments scoring zero. This indicates that while LSTM can effectively recognize the dominant neutral sentiment, it struggles with minority class identification due to data imbalance. The findings highlight the need for improved model training strategies and data augmentation to enhance classification performance across all sentiment categories. Overall, this study contributes to the integration of lexicon-based and deep learning approaches for sentiment analysis and offers valuable insights for educators and policymakers in optimizing the Merdeka Curriculum through data-informed decisions.

Linguistic predictors of response to internet-based written CBT for PTSD symptoms of 9/11 world trade center recovery workers and survivors.

There are millions of People Living with HIV/AIDS (PLWHA) globally and over the years, addressing their concerns has been topical for many stakeholders. It is a well-known and established fact that PLWHA are at increased risk of victimization and stigmatization. Unfortunately, the world experienced an outbreak of the COVID-19 pandemic that has led to strict social measures in many states. Thus, it is the goal of this research to study the impact that the outbreak and its mitigation measures have had on the PLWHA. Specifically, we sought to highlight their concerns from sentiments expressed on social media based on posted tweets. By combining machine learning (ML) techniques such as textual mining and thematic analysis, we determined 14 major themes as factors that are worth exploring. In this work, we originally extracted 2,839,091 tweets related to HIV/AIDS posted from March 2020 to April 2022. After initially doing data cleaning and preprocessing, we performed topic modeling using the Latent Dirichlet Allocation (LDA) topic model to extract 25 topics that are made up of 30 keywords each. The topics were then narrowed into 14 themes. The paper details the negative, positive, and neutral sentiment polarities which we highlight as concerning. These sentiments were determined using the Valence Aware Dictionary and sEntiment Reasoner (VADER) Sentiment Analysis Library with a 90% F1-score compared to TextBlob which showed a 53% F1-score. The research findings highlight issues affecting PLWHA during and post-pandemic such as high cost of medical care, late diagnosis of HIV, limited access to medications, stigmatization and victimization, absence of testing kits in hospitals, and lack of urgency in the development of vaccines or cure for HIV.

The research is prompted by the dearth of studies addressing public perceptions of various COVID-19 vaccines in Indonesia using extensive datasets spanning and a wide timeframe. This study examined public perception toward COVID-19 vaccines in Indonesia using a dataset of tweets. We further detect whether there are any changes in sentiment toward each type of vaccine. The five most commonly used vaccines in Indonesia (AstraZeneca, Moderna, Pfizer, Sinopharm, and Sinovac) were analyzed for sentiment using a lexicon-based method: Valence Aware Dictionary and Sentiment Reasoner (VADER), with changes in sentiment detected using Pruned Exact Linear Time (PELT). The 280,826 tweets collected between 2021 and 2022, 39% were positive, 18% were negative, and 43% were neutral. While Indonesian citizens generally responded positively and neutrally to each vaccine, with Sinopharm and Pfizer receiving the highest sentiment scores and AstraZeneca receiving the lowest, some change points in sentiment were associated with real-world events. Jakarta had the highest number of tweets (22%), while Maluku had the highest sentiment score (0.498). A significant positive correlation was also found between the total number of tweets and two variables: new cases of COVID-19 (r=0.9, p=0.001) and total new deaths caused by COVID-19 (r=0.8, p=0.008). Overall, the discussion of COVID-19 vaccines is still ongoing, and Indonesian citizens tend to respond neutrally and positively regardless of location or time.

The wealth of online reviews has grown exponentially, attracting the attention of many researchers who recognize their potential as a valuable source of customer feedback. Leveraging online reviews enables product designers to gain a deeper understanding of customer preferences and make informed decisions to improve product design. Traditionally, the major source of online reviews comes from e-commerce websites, such as Amazon and eBay. However, as social media platforms gain popularity, video reviews from these platforms are becoming an increasingly important source of customer feedback. Video reviews have several advantages over traditional textual reviews. They are more comprehensive, less likely to be fake, have greater coverage of the customer base, and contain more interaction in the form of comments. The paper presents a four-stage methodology to analyze video reviews from social media platforms as an alternative source of customer feedback. This involves collecting and preprocessing video reviews, extracting product features using latent Dirichlet allocation (LDA) models, analyzing sentiment using the valence aware dictionary and sentiment reasoner (VADER) package, and computing feature importance using SHAP values. To the best knowledge of the authors, very few literatures have investigated the feasibility of using video reviews as a substitute for traditional online reviews. Therefore, this paper contributes to the growing literature by demonstrating the viability of video reviews as an alternative source of online reviews to understand customer preferences and their implications for engineering design.

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics and stickers. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state of the art works exploit the text associated with a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user, which usually includes text useful to maximize the diffusion of the social post. This System will extract three views: visual view, subjective text view and objective text view of Flickr images and will give sentiment polarity positive, negative or neutral based on the hypothesis table. Subjective text view gives sentiment polarity using VADER (Valence Aware Dictionary and sEntiment Reasoner) and objective text view gives sentiment polarity with three convolution neural network models. This system implements VGG-16, Inception-V3 and ResNet-50 convolution neural networks with pre pre-trained ImageNet dataset. The text extracted through these three convolution networks is given to VADER as input to find sentiment polarity. This system implements visual view using a bag of visual word model with BRISK (Binary Robust Invariant Scalable Key points) descriptor. System has a training dataset of 30000 positive, negative and neutral images. All the three views’ sentiment polarity is compared. The final sentiment polarity is calculated as positive if two or more views gives positive sentiment polarity, as negative if two or more views gives negative sentiment polarity and as neutral if two or more views gives neutral sentiment polarity. If all three views give unique polarity then the polarity of the objective text view is given as output sentiment polarity.