Evaluation of IPRs using Modern Sentimental Analysis Methods in the Law Domain

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.

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.

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.

The Colour of Copyright

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

Sentiment analysis is the foremost task in Natural Language Processing to understand the user’s attitude (positive, neutral, or negative) by capturing their thoughts, opinions, and feeling about a particular product. This helps companies to fulfill customer satisfaction and make better future decisions about the product. Various techniques have been used in the literature forsentiment analysis, such as polarity scores, classifications, and automated sentiment analysis. In this paper, Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis tool has been employed on a Twitter dataset (downloaded from https://www.kaggle.com). The study aims to measure the performance of VADER sentiment while concatenating fourteen English language punctuations marks, including Exclamation (!), Comma (,), Full Stop (.), Question Mark (?), Round Brackets (), Curly Brackets {}, Square Brackets [], Colon (:), Apostrophe (‘), Dash (-), Hyphen (--), Semi-Colon (;), Slash (/), Quotation Mark (“ ”) and to observe whether the polarity (positive, neutral and negative) of a sentence changes or remains the same. After the analysis, the study found that Exclamation (!) maximizes the average positive polarity and average negative polarity and lowers the average neutralpolarity. The Hyphen (--) and Comma (,) increase the average positive and neutral polarity and decrease the aver-age negative polarity. For Round Brackets (), Curly Brackets {}, Square Brackets [], Colon (:), Apostrophe (‘), Dash (-), Semi-Colon (;), Slash (/) and Full Stop (.) the average positive and average neutral polarity decreases and average negative polarity increases.

Abstract: Automobile reviews provide a valuable opportunity for sentiment analysis in order to gain a better understanding of consumer opinions and improve product quality. As a result of the complexity of natural language and differences in user opinions, accurate and robust sentiment analysis by computer models remains challenging. Traditional machine learning algorithms such as Support Vector Machines (SVM) and Naive Bayes (NB) have been used for sentiment analysis on textual data. They can, however, be limited by the heterogeneity of user opinions and language variations. In contrast, deep learning models and natural language processing have shown great promise in sentiment analysis. In particular, the VADER (Valence Aware Dictionary and sentiment Reasoner) algorithm has been proposed as a powerful tool for sentiment analysis on social media texts, and has shown promising results in other domains as well. In this work, The aim is to achieve accurate sentiment analysis on automobile reviews by comparing the accuracy performance of SVM, NB, and the VADER algorithm.

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%.

While information technologies present organizations with opportunities to become more competitive, unsettled social norms and lagging legislation guiding the use of these technologies present organizations and individuals with ethical dilemmas. This paper presents two studies investigating the relationship between intellectual property and privacy attitudes, Machiavellianism and Ethical Ideology, and working in R&D and computer literacy in the form of programming experience. In Study 1, Machiavellians believed it was more acceptable to ignore the intellectual property and privacy rights of others. Programmers and R&D workers considered violating intellectual property rights more acceptable. Programmers did not consider violating privacy rights more acceptable, but R&D workers did. Finally, there was an interaction between Machiavellianism, programming and R&D. Machiavellians who also had programming experience or worked in R&D found violations of intellectual property much more acceptable. The effect of Machiavellianism on attitudes toward violations of privacy was enhanced by working in R&D, but not by programming experience. In Study 2, idealists believed it was less acceptable to ignore the intellectual property and privacy rights of others. Relativists found it more acceptable to violate intellectual property rights, though they did not consider it more acceptable to violate privacy rights. Those with programming experience were more accepting of intellectual property rights violations, but not of privacy violations. Finally, programming experience moderated the relationship between idealism, relativism and attitudes toward these unethical information practices. Implications for diminishing unethical behavior among Machiavellians, Relativists, programmers and those in R&D are discussed.

Intellectual property rights are legal rights that protect intellectual assets, which hold signifi cant value for individuals and even greater importance for the economy and society as a whole. Liability insurance against the infringement of intellectual property (IP) rights is becoming increasingly relevant as a mechanism for managing unforeseen risks arising from third-party proprietary claims and from unauthorized use of intellectual property rights. This paper fi rst examines the nature and scope of various intellectual property rights, alongside the advantages of liability insurance in cases of infringement of such rights. It then explores what may constitute the subject-matter of insurance coverage, based on the nature of intellectual property rights, general insurance principles, and contractual frameworks. The aim is to distinguish this specifi c type of liability insurance within the broader context of IP protection. Finally, the paper examines the scope of insurance coverage in relation to specifi c costs incurred when the insured appears as a defendant in legal proceedings, as well as when the insured takes actions to protect and enforce their intellectual property rights.

<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

BackgroundHIV remains a global challenge, with stigma, financial constraints, and psychosocial barriers preventing people living with HIV from accessing health care services, driving them to seek information and support on social media. Despite the growing role of digital platforms in health communication, existing research often narrowly focuses on specific HIV-related topics rather than offering a broader landscape of thematic patterns. In addition, much of the existing research lacks large-scale analysis and predominantly predates COVID-19 and the platform’s transition to X (formerly known as Twitter), limiting our understanding of the comprehensive, dynamic, and postpandemic HIV-related discourse.ObjectiveThis study aims to (1) observe the dominant themes in current HIV-related social media discourse, (2) explore similarities and differences between theory-driven (eg, literature-informed predetermined categories) and data-driven themes (eg, unsupervised Latent Dirichlet Allocation [LDA] without previous categorization), and (3) examine how emotional responses and temporal patterns influence the dissemination of HIV-related content.MethodsWe analyzed 191,972 tweets collected between June 2023 and August 2024 using an integrated analytical framework. This approach combined: (1) supervised machine learning for text classification, (2) comparative topic modeling with both theory-driven and data-driven LDA to identify thematic patterns, (3) sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) and the NRC Emotion Lexicon to examine emotional dimensions, and (4) temporal trend analysis to track engagement patterns.ResultsTheory-driven themes revealed that information and education content constituted the majority of HIV-related discourse (120,985/191,972, 63.02%), followed by opinions and commentary (23,863/191,972, 12.43%), and personal experiences and stories (19,672/191,972, 10.25%). The data-driven approach identified 8 distinct themes, some of which shared similarities with aspects from the theory-driven approach, while others were unique. Temporal analysis revealed 2 different engagement patterns: official awareness campaigns like World AIDS Day generated delayed peak engagement through top-down information sharing, while community-driven events like National HIV Testing Day showed immediate user engagement through peer-to-peer interactions.ConclusionsHIV-related social media discourse on X reflects the dominance of informational content, the emergence of prevention as a distinct thematic focus, and the varying effectiveness of different timing patterns in HIV-related messaging. These findings suggest that effective HIV communication strategies can integrate medical information with community perspectives, maintain balanced content focus, and strategically time messages to maximize engagement. These insights provide valuable guidance for developing digital outreach strategies that better connect healthcare services with vulnerable populations in the post–COVID-19 pandemic era.

A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine–Russia conflict

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.

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.