Valence Aware Dictionary And sEntiment Reasoner Research Articles

Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan

There is a limited understanding on the information-seeking behavior of international tourists during disaster response scenarios due to the lack of empirical studies on crisis communication in Japan. This study clarifies the topics generated from both international tourists and official Twitter accounts by applying the embedding Bidirectional Encoder Representations from Transformers (BERT) topic model and examines the temporal sentiment changes toward transportation and tourism using the sentiment scores obtained from topic-based Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis during disasters. A case study was conducted using Twitter data on Typhoons Faxai and Hagibis, which struck Japan in 2019. This study found differences in the topics generated among international tourists and officials in response and a continuous negative sentiment toward specific transportation services. The managerial implications of these findings regarding the use of social media in crisis communication in tourism are also discussed.

Sentiment analysis of letters of recommendation for a U.S. pain medicine fellowship from 2020 to 2023.

Letters of recommendation (LORs) are an important part of pain medicine fellowship applications that may be subject to implicit bias by the letter's author. This study evaluated letters of recommendation for applications to pain medicine fellowships in the United States to characterize biases and differences among applicants over four application cycles. This was a retrospective single-site cohort study. De-identified LORs were collected from 2020 to 2023 from one institution. The Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package scored positive LOR sentiment. In addition, the deep learning tool, Empath, scored LORs for 15 sentiments. Wilcoxon rank-sum and one-way ANOVA tests compared scores between applicant demographics: gender, race, medical school type, residency specialty, and chief resident status, as well as letter writers' academic position. Nine hundred and sixty-four applications were studied over four application cycles. Program directors wrote fewer words (p = 0.020) and less positively (p < 0.001) compared to department chairs and letter writers with neither position. Department chairs wrote with less "negative emotion" compared to both program directors and writers with neither position (p < 0.001). Anesthesiologist applicants received more letters highlighting "achievement" (p < 0.001) while PM&R applicants submitted letters with less "negative emotion" (p < 0.001) compared to other specialties. Chief residents' letters scored higher in "leader" sentiment (p < 0.001) and lower in "negative emotion" (p < 0.001). Linguistic content did not favor certain genders or races over others. However, disparities in LORs do exist depending on an applicant's specialty and chief resident status, as well as the academic status of the letter writer.

Gender Differences in Letters of Recommendations and Personal Statements for Neurotology Fellowship over 10 Years: A Deep Learning Linguistic Analysis.

Personal statements (PSs) and letters of recommendation (LORs) are critical components of the neurotology fellowship application process but can be subject to implicit biases. This study evaluated general and deep learning linguistic differences between the applicant genders over a 10-year span. Retrospective cohort. Two institutions. PSs and LORs were collected from 2014 to 2023 from two institutions. The Valence Aware Dictionary and Sentiment Reasoner (VADER) natural language processing (NLP) package was used to compare the positive or negative sentiment in LORs and PSs. Next, the deep learning tool, Empath, categorized the text into scores, and Wilcoxon rank sum tests were performed for comparisons between applicant gender. Among 177 applicants over 10 years, 120 were males and 57 were females. There were no differences in word count or VADER sentiment scores between genders for both LORs and PSs. However, among Empath sentiment categories, male applicants had more words of trust ( p = 0.03) and leadership ( p = 0.002) in LORs. Temporally, the trends show a consistently higher VADER sentiment and Empath "trust" and "leader" in male LORs from 2014 to 2019, after which there was no statistical significance in sentiment scores between genders, and females even have higher scores of trust and leadership in 2023. Linguistic content overall favored male applicants because they were more frequently described as trustworthy and leaders. However, the temporal analysis of linguistic differences between male and female applicants found an encouraging trend suggesting a reduction of gender bias in recent years, mirroring an increased composition of women in neurotology over time.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

Data 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. This 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. We 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. Using 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. BERTopic 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.

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

Comparing the accuracy of ANN with transformer models for sentiment analysis of tweets related to COVID-19 Pfizer vaccines

The current study underscores the critical importance of understanding public sentiment towards vaccination efforts, particularly to achieve the widespread vaccine uptake necessary for public health security, especially in light of pandemics like COVID-19. Despite the clear critical role vaccinations play in mitigating pandemic spread, vaccine hesitancy persists as a formidable challenge. This research focuses on analyzing sentiments related to the Pfizer-BioNTech COVID-19 vaccine using Natural Language Processing (NLP) techniques, offering a comparative assessment of Artificial Neural Network (ANN) frameworks and transformer-based models. The study employs the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment quantification and TensorFlow for text vectorization. Within the ANN domain, both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are investigated, alongside the ALBERT and Distilbert models as a representative of transformer-based architectures. The empirical analysis reveals a distinct advantage of transformer-based models over ANN frameworks in accuracy, with the ALBERT model exhibiting exceptional skill in classifying sentiments of tweets concerning the COVID-19 vaccine by Pfizer. The study meticulously employs Receiver Operating Characteristic (ROC) curve analysis to rank the performance of the evaluated models in sentiment classification, establishing ALBERT as the foremost model, followed in order by LSTM-CNN, Distilbert, CNN, and LSTM models. The ALBERT model demonstrates outstanding performance across critical evaluation metrics. The findings advocate for the strategic use of advanced NLP technologies in public health initiatives to better understand and respond to public attitudes towards vaccination, ultimately contributing to improved health outcomes and pandemic preparedness.

Sentiment Analysis of Amazon Reviews using NLTK Vader and Robert

With the advancement of technology, huge volume of data is exchanged every-day, example - from social media sites like Instagram, Twitter, or e-commerce sites like Amazon, Flipkart etc. The human brain is unable to process this large amount of data (mainly out of which is unstructured) and this results in the development of technology which do the work of humans. One such methodology is Sentiment Analysis. Sentiment analysis plays a crucial role in understanding public opinion and consumer behavior. In this paper, we conduct a comparative study of sentiment analysis on Amazon product reviews using two different sentiment analysis approaches: VADER (Valence Aware Dictionary and Sentiment Reasoner) and RoBERTa (Robustly Optimized BERT Approach). We explore the effectiveness of these models in capturing the sentiment expressed in Amazon reviews, considering various aspects such as accuracy, computational efficiency, and generalization capabilities. Our findings provide insights into the strengths and limitations of each approach, contributing to the advancement of sentiment analysis methodologies. Keywords Sentiment Analysis, Roberta, Vader, Machine Learning.

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

&lt;span lang="EN-US"&gt;Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and &lt;br /&gt; 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 &lt;br /&gt; 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%.&lt;/span&gt;

Textual sentiment analysis and description characteristics in crowdfunding success: The case of cybersecurity and IoT industries

Crowdfunding platforms offer entrepreneurs the opportunity to evaluate their technologies, validate their market, and raise funding. Such platforms also provide technologies with an opportunity to rapidly transition from research to market, which is especially crucial in fast-changing industries. In this study, we investigated how the sentiments expressed in the text of the project campaigns and project characteristics influence the success of crowdfunding in innovative industries such as cybersecurity and the Internet of Things (IoT). We examined 657 cybersecurity and Internet of Things (IoT) projects between 2010 and 2020 that were promoted on Kickstarter and IndieGoGo, two rewards-based crowdfunding platforms. We extracted technological topic attributes that may influence project success and measured the sentiments of project descriptions using a Valence Aware Dictionary and sEntiment Reasoner (VADER) model. We found that the sentiment of the description and the textual topic characteristics are associated with the success of funding campaigns for cybersecurity and IoT projects.

Feeling analysis on allergen immunotherapy on Twitter using an unsupervised machine learning model

Analyze feelings about allergen-specific immunotherapy on Twitter using the VADER model VADER (Valence Aware Dictionary and sEntiment Reasoner) model. tweets related to specific allergen immunotherapy were obtained through the Twitter Application Programming Interface (API). The keywords "allergy shot" were used between January 1, 2012, and December 31, 2022. The data was processed by removing URLs, usernames, hashtags, multiple spaces, and duplicate tweets. Subsequently, a sentiment analysis was performed using the VADER model. A total of 34,711 tweets were retrieved, of which 1928 were eliminated. Of the remaining 32,783 tweets, 32.41% expressed a negative sentiment, 31.11% expressed a neutral sentiment, and 36.47% expressed a positive sentiment, with an average polarity of 0.02751 (neutral) over the 11-year period. The average polarity of tweets about allergen-specific immunotherapy is neutral over the 11 years analyzed. There was an annual increase in the average polarity over the years, with 2017, 2018, and 2022 having positive polarity averages. Additionally, the number of tweets decreased over time.

Impact of Social Media Sentiments on Stock Market Behavior: A Machine Learning Approach to Analyzing Market Dynamics

Social media has become a valuable tool for informed decision-making. This research delves into the influence of Twitter sentiments on the stock market’s movements and price fluctuations, specifically focusing on Tesla Inc and the tweets of Elon Musk. A combination of deductive and inductive reasoning approaches is used to explore the intricate relationship between the social media platform and the stock market. Methodologically, the Twitter data undergoes rigorous processing to derive features for the machine learning predictive model, and the sentiments are extracted using the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. This study emphasizes the usefulness of social media in predictive modeling while underscoring the importance of evaluating data reliability considering challenges such as spam tweets and geographical relevance. Multiple machine learning models are tested against four distinct datasets addressing the high stock price volatility. XG Boost and Random Forest Regressor emerge as the most effective performers, particularly when moving averages are included, showing enhanced performance. This research establishes an evident correlation between social media sentiments and stock market movements, however with limited predicting power. It is also noted that integrating traditional financial metrics enriches the understanding of stock market dynamics while enhancing the model’s predictability.

Transfer learning approach for identifying negative sentiment in tweets directed to football players

In recent times, the rising cases of racial abuse toward football players are gravely concerning. This trend is becoming a recurrent decimal on social networks with little or no proactive measures to serve as a deterrent to others in most cases. There was a public outcry due to massive racial abuse remarks targeting some England footballers during the Euro2020 final between England and Italy on social media. This motivated us to investigate and recommend a better solution to the social pandemic through the application of transfer learning. This will go a long way to maintain unity in diversity in the game of football. Tweets with related hashtags to the football match were scraped. The researchers framed this problem as a multi-class classification task. The Valence Aware Dictionary and sEntiment Reasoner (VADER) compound score was employed to label the dataset as positive (POS), negative (NEG), and neutral (NEU). Seven pre-trained models were fine-tuned and corresponding models were built for identifying negative sentiments toward the football players. Three contributions were made – a review of literature on racial abuse targeting footballers, a new dataset to further research in this direction and a robust transformer-based model identified for policing Twitter during football games. Among the models built, the distilled version of Bidirectional Encoder Representations from Transformer (DistilBERT) proved superior with an F1-score of 0.99.

Analyzing public perception toward COVID-19 vaccines in Indonesia

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.

Islamic Crowdfunding: A Twitter Sentiment Analysis

This research aims to measure public sentiment related to Islamic crowdfunding on the Twitter social media platform. The research method involves the extraction and classification of tweet data using a Python Library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The research utilized tweet data posted in the past one year. The results showed fluctuations and decreases in the number of tweets discussing Islamic crowdfunding. Word cloud analysis on keywords shows that in positive sentiments, words such as "crowdfunding platform," "crowdfunding impact," "crowdfunding," "Islamic finance," and "inclusivity Islamic finance" dominate the visualization. Overall, the majority of tweets expressed a positive attitude towards Islamic crowdfunding, with 44.0% positive sentiment. A total of 47.2% of tweets showed neutral sentiment, while 8.7% showed negative sentiment. These results illustrate that people generally give positive support to the concept of Islamic crowdfunding, although there is still a small proportion of tweets that express less favorable views. This research provides valuable insights into people's perceptions and responses to Islamic crowdfunding in cyberspace.

Sentiment Analysis of Student Evaluation for Teachers Using Valence-Aware Dictionary and Sentiment Reasoner

This study analyzed the quality of teaching using the Student Evaluation of Teaching (SET) and the Valence Aware Dictionary and Sentiment Reasoner (VADER) to assess textual comments, providing a comprehensive view of teaching effectiveness beyond numerical ratings. The objectives were to identify faculty strengths and areas for improvement based on student feedback, analyze sentiment toward teaching methods, and determine the optimal number of clusters within the dataset. The analysis included 28,222 student comments from three semesters, preprocessed through tokenization, stopword removal, part-of-speech tagging, and lemmatization. A word cloud visualized common terms, while K-means clustering and the Elbow method identified five as the optimal number of clusters. Results indicate that most comments are positive, emphasizing effective teaching methods' role in creating a positive educational experience. The findings suggest integrating machine learning with VADER and expanding the dataset for broader insights. Institutions should develop effective teaching strategies, prioritizing regular feedback collection and analysis.

Sentiment analysis of U.S. News & World Report Best Children's Hospital urology rankings: A difference in positivity between the public and academic worlds

Initiated in 2009, the U.S. News & World Report (USNWR) pediatric urology rankings aim to guide patients and families towards high-quality urologic care. Despite this, the pediatric urology community remains divided, with significant debate over the rankings' accuracy, utility, and potential for misleading information. While some professionals argue for a collective opt-out from these rankings, citing these concerns, others highlight their positive impact on patient care, hospital benchmarking, and financial support. Recognizing the lack of formal evaluation on how these rankings are viewed beyond the pediatric urology community, this research endeavors to fill the gap through sentiment analysis of public news articles and academic publications. We captured news articles from Google News and academic papers from Ovid Medline and Embase, focusing specifically on content related to the USNWR pediatric urology rankings from 2009 to 2023. Sentiment analysis was conducted using the Valence Aware Dictionary and Sentiment Reasoner (VADER) package on both news and academic texts, aiming to capture the overall sentiment through a compound score derived from the presence of sentiment-laden words. Sensitivity analysis was performed using TextBlob Pattern Analyzer tool. The analysis revealed a significant divergence in sentiment between news articles and academic literature. News articles exhibited a predominantly positive sentiment, with an average compound score of 0.681, suggesting a general approval or celebration of the rankings in the public sphere. Conversely, academic literature showed a more moderate sentiment, with an average score of 0.534, indicating a nuanced perspective that includes both positive views and critical reflections on the rankings. Sensitivity analysis confirmed this observation (Figure). This difference may reflect the distinct nature of news media and academic discourse. While news outlets may prioritize celebratory narratives that align with public interest and institutional pride, academic discussions tend to offer a balanced view that critically assesses both the merits and limitations of the rankings. This discrepancy underscores the complexity of interpreting and acting upon the rankings within the pediatric urology community. While the USNWR pediatric urology rankings are generally received positively by the public, as reflected in news media, the academic community presents a more reserved sentiment. These findings suggest the need for ongoing dialogue and research to understand the implications of these rankings fully. It also calls for a strategic approach to address the concerns and perceptions of healthcare professionals, aiming to leverage the rankings in a way that truly benefits patient care and informed decision-making.

Enhancing Faculty Performance Appraisals with Sentiment Analysis and Data Visualization for Evaluating Teaching Effectiveness

An essential part of educational institutions is evaluating faculty performance, which provides insightful views on the effectiveness of instruction and the standard of education. Understanding students’ sentiments toward faculty members is essential to fostering a supportive learning environment and advancing the quality of instruction. This study examines how faculty members’ performance is evaluated utilizing word clouds, sentiment analysis, and visualization tools. The researchers employ VADER (Valence Aware Dictionary and Sentiment Reasoner), a sentiment analysis tool, to ascertain the attitudes expressed in the students’ remarks. Word clouds were created to graphically depict the words that appear most frequently in these comments. Finally, we computed the mean rating for each question and presented our findings through informative graphs. Our research aims to reduce the disparity between traditional evaluation methods and contemporary digital approaches. Using sentiment analysis’s analytical powers and visualization’s communicative potential, we hope to improve the efficacy and granularity of teacher performance evaluations. Ultimately, this will foster a culture of excellence and ongoing development in higher education.

Social Media Sentiment Analysis on Waqf and Education

This research aims to measure public sentiment towards waqf education by using primary data derived from Twitter tweets. Sentiment measurement utilizes a Python Library software called VADER (Valence Aware Dictionary and Sentiment Reasoner) to classify the sentiment in each tweet analyzed. The results revealed that India was the location with the highest tweet activity around waqf education, reaching a total of 656 tweets. The highest peak of tweet activity occurred in 2020, with 170 tweets during the study period. In terms of sentiment polarity, the majority of tweets showed positive sentiment (48.3%), followed by neutral sentiment (27.0%), and negative sentiment (24.8%). The importance of these findings lies in the in-depth understanding of how people respond to and perceive waqf education. This sentiment analysis can provide valuable insights for decision makers and relevant stakeholders in developing and improving waqf education practices in the future

Mining Netizen's Opinion on Sustainable Agriculture: Sentiment Analysis of Twitter Data

This research aims to measure public sentiment related to sustainable agriculture on the Twitter social media platform. The research method involves the extraction and classification of tweet data using a Python Library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The research utilized tweet data posted in the past one year. The results showed fluctuations and decreases in the number of tweets discussing sustainable agriculture. The location with the most tweet activity around sustainable agriculture was Brussels, Belgium, with 642 tweets during the observation period. Word cloud analysis on keywords showed that in positive sentiments, words such as "food security" and "climate change" dominated the visualization. On the other hand, in negative sentiments, words such as "farmer" and "private farmland" appeared more frequently. Overall, the majority of tweets expressed a positive attitude towards sustainable agriculture, with 68.5% positive sentiment. A total of 22.3% of tweets showed neutral sentiments, with no strong positive or negative tendencies. Only 9.1% of tweets contained negative sentiment, indicating that a small proportion of tweets expressed less favorable views towards sustainable agriculture.

News Classification and Categorization with Smart Function Sentiment Analysis

Search engines are tools used to find information on the Internet. Since the web has a plethora of websites, the engine queries the majority of active sites and builds a database organized according to keywords utilized in the search. Because of this, when a user types a few descriptive words on the home page of the search engine, the search function lists websites corresponding to these keywords. However, there are some problems with this search approach. For instance, if a user wants information about the word Jaguar, most search results are animals and cars. This is a polysemic problem that forces search engines to always provide the most popular but not the most relevant results. This article presents a study of using sentiment technology to help news classification and categorization and improve the classification accuracy. We have introduced a smart search function embedded into a search engine to tackle polysemic issues and record relevant results to determine their sentimentality. Therefore, this study presents a topic that involves several aspects of natural language processing (NLP) and sentiment analysis for news categorization and classification. A web crawler was used to collect British Broadcasting Corporation (BBC) news across the Internet, carried out preprocessing of text by using NLP, and applied sentiment analysis methods to determine the polarity of the processed text data. The sentimentality represents negative, positive, or neutral polarities assigned by the sentiment analysis algorithms. The research utilized the BBC news site to collect different information using a web crawler and a database to explore the sentimentality of BBC news. The natural language toolkit (NLTK) and BM25 indexed and preprocessed patterns in the database. The experimental results depict the proposed search function surpassing normal search with an accuracy rate of 85%. Moreover, the results show a negative polarity of BBC news using the Sentistrength algorithm. Furthermore, the Valence Aware Dictionary and sEntiment Reasoner (VADER) was the best-performing sentiment analysis model for news classification. This model obtained an accuracy of 85% using data collected with the proposed smart function.