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1
Dhole, Yash Bhausaheb, Devesh Rampravesh Sharma, Dr Sanjay Patil, and Prof Deepali Chavan. "Unveiling Market Sentiments: Finbert-Powered Analysis of Stock News Headlines." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38254.
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This document Sentiment analysis based on news and headlines is a big part of financial markets. Through the utilization of Hugging Face and FinBERT-a specialized model for financial sentiment analysis-and advanced natural language processing techniques, this study makes use of the flexibility of Hugging Face. This study concentrates on pre-processing techniques and the implementation of a model, emphasizing the critical evaluation and correction of inherent biases in sentiment analysis. Results of the experiment show that FinBERT is effective in addressing and reducing biases while extracting diverse sentiments from stock market headlines. This study emphasizes the importance of bias-conscious sentiment analysis for making more informed decisions in financial markets. It highlights the importance of advanced natural language processing models (NLP) like FinBERT and powerful frameworks like Hugging Face. Key Words: optics, Financial Sentiment Analysis, FinBERT, Hugging Face, Natural Language Processing (NLP), Bias Mitigation, Stock Market, Sentiment Analysis, Machine Learning, News Headlines, Investment Decision-making, Data Pre-processing, Neural Networks, Market Sentiments, Text Classification, NLP Frameworks
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Kirange, D. K., and Ratnadeep R. Deshmukh. "Sentiment Analysis of News Headlines for Stock Price Prediction." COMPUSOFT: An International Journal of Advanced Computer Technology 05, no. 03 (2016): 2080–84. https://doi.org/10.5281/zenodo.14791593.
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Stock market data analysis needs the help of artificial intelligence and data mining techniques. The volatility of stock prices depends on gains or losses of certain companies. News articles are one of the most important factors which influence the stock market. This study basically shows the effect of emotion classification of financial news to the prediction of stock market prices. In order to find correlation between sentiment predicted from news and original stock price and to test efficient market hypothesis, we plot the sentiments of two companies (Infosys and Wipro) over a period of 10 years. For emotion classification, various classifiers such as Naive Bayes, Knn and SVM are evaluated. The comparison between positive sentiment curve and stock price trends reveals co-relation between them.
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Meng, Fanshuai, Amanzhuli Yeerlan, and Zihan Zhang. "Sentiment Analysis of News Headlines and Stock Price Prediction." Applied and Computational Engineering 135, no. 1 (2025): 245–50. https://doi.org/10.54254/2755-2721/2025.21207.
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Investing methods and decision-making of the stock market are driven by lots of factors, among all these factors, the significant impact of news should not be overlooked. This article combines machine learning algorithms to analyze sentiment analysis based on daily top 25 news titles and the up and down condition of stock price, using CountVectorizer and Term Frequency-Inverse Document Frequency (TF-IDF) to extract textual feature, then implementing Random Forest (RF) and Logistic Regression (LR) to train, test, and carry out prediction. From the results of the classification report, the performance and efficiency of each combination are analyzed and compared to draw conclusions. The results show that using TF-IDF instead of CountVectorizer can bring higher accuracy in this large and high precision text categorization task. Even if LR can more accurately recall news of stock price declines in binary classification problems, RF has the best performance in the balance of accuracy and classification.
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Yadav, Vijay, and Subarna Shakya. "Sentiment Analysis and Topic Modeling on News Headlines." Journal of Ubiquitous Computing and Communication Technologies 4, no. 3 (2022): 204–18. http://dx.doi.org/10.36548/jucct.2022.3.008.
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Sentiment analysis and topic modeling has wide range of applications from medical to entertainment industry, corporates, politics and so on. News media play vital role in shaping the views of public towards any product or people. The dataset used for this work is news headlines dataset of one of the leading new portals of India i.e., Times of India. This research aims to perform comparative study of both supervised and unsupervised learning for text analysis and use the best performing models in both the category for prediction of sentiment and topic classification of news headlines. For sentiment analysis, supervised techniques like Machine learning ensemble model and Bi-LSTM have used. Similarly, unsupervised techniques like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) have been for topic modeling.
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Naury, Chairullah, Dhomas Hatta Fudholi, and Ahmad Fathan Hidayatullah. "Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (2021): 24. http://dx.doi.org/10.30865/mib.v5i1.2556.
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The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.
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Rahmadian, Adhi. "Public Sentiment Towards Mandatory Halal Certification: A Large Language Model (LLM) Approach." Likuid Jurnal Ekonomi Industri Halal 4, no. 2 (2024): 1–15. http://dx.doi.org/10.15575/likuid.v4i2.35185.
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This study analyzes public sentiment towards mandatory halal certification in Indonesia, as mandated by Law No. 33/2014 and its revision in Government Regulation No. 39/2021. Using the Large Language Model (LLM) approach, sentiment analysis was conducted on a dataset consisting of 320 samples of headlines from various electronic media platforms, published between 2019 and 2023. The LLM model, employing the RoBERTa architecture, was trained on an Indonesian language dataset and optimized for sentiment classification tasks. Data preprocessing included web scraping, data cleansing, and text vectorization using Term Frequency-Inverse Document Frequency (TF-IDF) techniques and cosine-similarity. The model demonstrated a confidence score of the classifications mean of 87.35% and median 96.12% in classifying the news headlines. Results revealed a predominant positive sentiment (57%) towards halal certification, indicating public awareness of its significances. However, negative sentiments (26%) highlighted challenges faced by Small and Medium Enterprises (SMEs), including high costs and lack of understanding about the certification process. The temporal analysis showed an increase in halal-related news coverage following the implementation of new regulations. This study contributes to the understanding of public perception towards regulatory changes in the halal industry and demonstrates the effectiveness of LLM-based sentiment analysis in comprehending public opinions. The findings provide valuable insights for policymakers and businesses in addressing the potential and challenges in implementing mandatory halal certification.
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Ms., Kalyani D. Gaikwad* Prof. Sonawane V.R. "OPINION MINING AND SENTIMENT ANALYSIS TECHNIQUES: A RECENT SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 1003–6. https://doi.org/10.5281/zenodo.225397.
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Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. The difficulties of performing sentiment analysis in this domain can be overcome by leveraging on common-sense knowledge bases. Opinion Mining is an area of text classification which continuously gives its contribution in research field. The main objective of Opinion mining is Sentiment Classification i.e. to classify the opinion into positive or negative classes. Further, most of the researchers implement the opinion mining by separating out the adverb-adjective combination present in the statements or classifying the verbs of statements. Opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. RSS uses a family of standard web feed formats to publish frequently updated information: blog entries, news headlines.
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Pradipta, Nathanael Yudhistira, and Hari Soetanto. "Sentiment Classification of General Election 2024 News Titles on Detik.com Online Media Website Using Multinominal Naive Bayes Method." Journal of Applied Science, Engineering, Technology, and Education 6, no. 1 (2024): 43–55. https://doi.org/10.35877/454ri.asci2754.
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In The 2024 elections will produce a variety of political tendencies in society, with different opinions regarding its implementation and conditions. The role of news headlines is very important in influencing speculation and public responses to certain topics or issues. This study investigates the sentiment conveyed in news headlines about the 2024 Election using the Multinomial Naïve Bayes approach. Data was gathered from Detik.com, an online media platform, utilizing search terms “Pemilu 2024” and “Pemilihan Umum 2024” through web scraping methods. The data preprocessing involved converting to lowercase, tokenization, punctuation removal, stopword elimination, normalization, and stemming. Training data comprised 90% while 10% was allocated for testing. Analysis showed an accuracy of 83.59% using CountVectorizer for data transformation. Beyond sentiment classification, the research also examines how political processes shape media narratives and influence public perception. The implications highlight the impact of online media sentiment on digital democracy dynamics, providing valuable insights for political practitioners, policymakers, and media scholars. This study is not only academically significant but also offers practical insights for enhancing public understanding of the electoral process.
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Mochamad Alfan Rosid, Siti Nur Haliza, Yulian Findawati, and Uce Indahyanti. "Sarcasm Detection in News Headline Dataset with Ensemble Deep Learning Method." JOINCS (Journal of Informatics, Network, and Computer Science) 6, no. 2 (2023): 47–52. http://dx.doi.org/10.21070/joincs.v6i2.1628.
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Sarcasm, a prevalent linguistic device, is frequently used in public discourse, often causing offence and distress to the listener. The complexity inherent in detecting sarcasm is a significant and ongoing challenge in the field of sentiment analysis research. The widespread use of this phenomenon in diverse conversational contexts further complicates its identification in data sets full of human interactions. Deficiencies in methodologies for distinguishing such statements adversely affect the performance of sentiment analysis, especially in distinguishing negative, positive or neutral sentiments. Inaccuracies in sarcasm detection can affect the classification results of sentiment analysis. Therefore, sentiment analysis seeks to categorise sarcastic sentences that, despite appearing positive, actually contain negative meanings. This research aims to build a deep learning ensemble stack model. The basic deep learning methods used are Bidirectional Gated Recurrent Unit (BiGRU) and Convolutional Neural Network (CNN). LightGBM is used to perform stack ensemble of deep learning methods. The dataset used comes from the Kaggle website and consists of English headlines. The findings show that the stack ensemble method outperforms BiGRU and CNN, evidenced by an accuracy rate of 91.2% and an F1 score of 90.2%. Therefore, from the above discussion, it can be concluded that the LightGBM method emerges as the optimal solution for sarcasm detection
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Dahal, Keshab Raj, Ankrit Gupta, and Nawa Raj Pokhrel. "Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning." Econometrics 12, no. 2 (2024): 16. http://dx.doi.org/10.3390/econometrics12020016.
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Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is now feasible. This study aims to construct a predictive model using news headlines to predict stock market movement direction. It conducts a comparative analysis of five supervised classification machine learning algorithms—logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN)—to predict the next day’s movement direction of the close price of the Nepal Stock Exchange (NEPSE) index. Sentiment scores from news headlines are computed using the Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob sentiment analyzer. The models’ performance is evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all five models perform equally well when using sentiment scores from the TextBlob analyzer. Similarly, all models exhibit almost identical performance when using sentiment scores from the VADER analyzer, except for minor variations in AUC in SVM vs. LR and SVM vs. ANN. Moreover, models perform relatively better when using sentiment scores from the TextBlob analyzer compared to the VADER analyzer. These findings are further validated through statistical tests.
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Alanazi, Saad Awadh, Ayesha Khaliq, Fahad Ahmad, et al. "Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques." International Journal of Environmental Research and Public Health 19, no. 15 (2022): 9695. http://dx.doi.org/10.3390/ijerph19159695.
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Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.
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Hutami, Widiananda Putri, Hari Wijayanto, and Itasia Dina Sulvianti. "Penerapan Support Vector Machine dengan SMOTE Untuk Klasifikasi Sentimen Pemberitaan Omnibus Law Pada Situs CNNIndonesia.com." Xplore: Journal of Statistics 11, no. 1 (2022): 26–35. http://dx.doi.org/10.29244/xplore.v11i1.852.
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The declaration of the omnibus law reaped the pros and cons in the community. In a situation like this, the media should be neutral. One of the media that still maintains neutrality is Detik (Rumata 2017). Detik owns several channels such as detikNews, detikFinance, and CNN Indonesia. In this study, the neutrality of the CNN Indonesia media as part of Detik will be studied based on the tendency of sentiment on the omnibus law-related news. Sentiment analysis is used to examine the trend of opinion on news headlines. In conducting sentiment analysis, a method that supports classification is needed. The classification method that will be used in this research is the Support Vector Machine (SVM). There is an imbalance of data in the three categories of sentiment so that the Synthetic Minority Oversampling Technique (SMOTE) method is used to overcome this imbalance. The omnibus law tends to be reported neutrally by CNNIndonesia.com site. The one vs all method has a better classification result than the one vs one method. The application of SMOTE only gives slightly better results than data classification without the application of SMOTE because the imbalance in the data is not too extreme. Modeling using the one vs all method with SMOTE and distribution of data 90% train data 10% test data gives the best classification results with a macro average f1-score of 60,33%.
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Diaz Jr., Manuel O. "A Domain-Specific Evaluation of the Performance of Selected Web-based Sentiment Analysis Platforms." International Journal of Software Engineering and Computer Systems 9, no. 1 (2023): 1–9. http://dx.doi.org/10.15282/ijsecs.9.1.2023.1.0105.
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There is now an increasing number of sentiment analysis software-as-a-service (SA-SaaS) offerings in the market. Approaches to sentiment analysis and their implementation as SA-SaaS vary, and there really is no sure way of knowing what SA-SaaS uses which approach. For potential users, SA-SaaS products are black boxes. Black boxes, however, can be evaluated using a set of standard input and a comparison of the output. Using a test data set drawn from human annotated samples in existing studies covering sentiment polarity of news headlines, this study compares the performance of selected popular and free (or at least free-to-try) SA-SaaS in terms of the accuracy, precision, recall and specificity of the sentiment classification using the black box testing methodology. SentiStrength, developed at the University of Wolverhampton in the UK, emerged as consistent performer across all metrics.
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He, Jiawei, Roman N. Makarov, Jake Tuero, and Zilin Wang. "Performance evaluation metric for statistical learning trading strategies." Data Science in Finance and Economics 4, no. 4 (2024): 570–600. https://doi.org/10.3934/dsfe.2024024.
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<p>We analyze how the sentiment of financial news can be used to predict stock returns and build profitable trading strategies. Combining the textual analysis of financial news headlines and statistical methods, we build multi-class classification models to predict the stock return. The main contribution of this paper is twofold. Firstly, we develop a performance evaluation metric to compare multi-class classification methods, taking into account the precision and accuracy of the models and methods. By maximizing the metric, we find optimal combinations of models and methods and select the best approach for prediction and decision-making. Secondly, this metric enables us to construct profitable option trading strategies, which can also be used as an assessment tool to analyze models' prediction power. We apply our methodology to historical data from Apple stock and financial news headlines from Reuters from January 1, 2012 to May 31, 2019. During validation (May 31, 2018, to May 31, 2019), our models consistently outperformed the market, with two-class one-stage models yielding returns between 30% and 45%, compared to the S &amp; P500 index's 1.73% return over the same period.</p>
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Hasan, Mahmudul, Tanver Ahmed, Md Rashedul Islam, and Md Palash Uddin. "Leveraging textual information for social media news categorization and sentiment analysis." PLOS ONE 19, no. 7 (2024): e0307027. http://dx.doi.org/10.1371/journal.pone.0307027.
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The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources and slowing inference times, making deployment difficult in resource-limited environments. In this paper, we process both structured and unstructured data, determining the polarity of text using the TextBlob scheme to determine the sentiment of news headlines. We propose a Stochastic Gradient Descent (SGD)-based Ridge classifier (RC) for blending SGDR with an advanced string processing technique to effectively classify news articles. Additionally, we explore existing supervised and unsupervised ML algorithms to gauge the effectiveness of our SGDR classifier. The scalability and generalization capability of SGD and L2 regularization techniques in RCs to handle overfitting and balance bias and variance provide the proposed SGDR with better classification capability. Experimental results highlight that our string processing pipeline significantly boosts the performance of all ML models. Notably, our ensemble SGDR classifier surpasses all state-of-the-art ML algorithms, achieving an impressive 98.12% accuracy. McNemar’s significance tests reveal that our SGDR classifier achieves a 1% significance level improvement over K-Nearest Neighbor, Decision Tree, and AdaBoost and a 5% significance level improvement over other algorithms. These findings underscore the superior proficiency of linear models in news categorization compared to tree-based and nonlinear counterparts. This study contributes valuable insights into the efficacy of the proposed methodology, elucidating its potential for news categorization and sentiment analysis.
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Pan, Ronghao, José Antonio García-Díaz, Francisco Garcia-Sanchez, and Rafael Valencia-García. "Evaluation of transformer models for financial targeted sentiment analysis in Spanish." PeerJ Computer Science 9 (May 9, 2023): e1377. http://dx.doi.org/10.7717/peerj-cs.1377.
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Nowadays, financial data from social media plays an important role to predict the stock market. However, the exponential growth of financial information and the different polarities of sentiment that other sectors or stakeholders may have on the same information has led to the need for new technologies that automatically collect and classify large volumes of information quickly and easily for each stakeholder. In this scenario, we conduct a targeted sentiment analysis that can automatically extract the main economic target from financial texts and obtain the polarity of a text towards such main economic target, other companies and society in general. To this end, we have compiled a novel corpus of financial tweets and news headlines in Spanish, constituting a valuable resource for the Spanish-focused research community. In addition, we have carried out a performance comparison of different Spanish-specific large language models, with MarIA and BETO achieving the best results. Our best result has an overall performance of 76.04%, 74.16%, and 68.07% in macro F1-score for the sentiment classification towards the main economic target, society, and other companies, respectively, and an accuracy of 69.74% for target detection. We have also evaluated the performance of multi-label classification models in this context and obtained a performance of 71.13%.
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Zhao, Futao, Zhong Yao, Jing Luan, and Hao Liu. "Inducing stock market lexicons from disparate Chinese texts." Industrial Management & Data Systems 120, no. 3 (2019): 508–25. http://dx.doi.org/10.1108/imds-04-2019-0254.
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Purpose The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse Chinese media outlets. Design/methodology/approach This paper presents a novel method to automatically generate financial lexicons using a unique data set that comprises news articles, analyst reports and social media. Specifically, a novel method based on keyword extraction is used to build a high-quality seed lexicon and an ensemble mechanism is developed to integrate the knowledge derived from distinct language sources. Meanwhile, two different methods, Pointwise Mutual Information and Word2vec, are applied to capture word associations. Finally, an evaluation procedure is performed to validate the effectiveness of the method compared with four traditional lexicons. Findings The experimental results from the three real-world testing data sets show that the ensemble lexicons can significantly improve sentiment classification performance compared with the four baseline lexicons, suggesting the usefulness of leveraging knowledge derived from diverse media in domain-specific lexicon generation and corresponding sentiment analysis tasks. Originality/value This work appears to be the first to construct financial sentiment lexicons from over 2m posts and headlines collected from more than one language source. Furthermore, the authors believe that the data set established in this study is one of the largest corpora used for Chinese stock market lexicon acquisition. This work is valuable to extract collective sentiment from multiple media sources and provide decision-making support for stock market participants.
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Sharma, Dilip Kumar, Bhuvanesh Singh, Saurabh Agarwal, Nikhil Pachauri, Amel Ali Alhussan, and Hanaa A. Abdallah. "Sarcasm Detection over Social Media Platforms Using Hybrid Ensemble Model with Fuzzy Logic." Electronics 12, no. 4 (2023): 937. http://dx.doi.org/10.3390/electronics12040937.
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A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.
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B., Ravikiran, and Srinivasu Badugu Dr. "Sarcasm Detection in Telugu Language Text Using Deep Learning." International Journal for Research Trends and Innovation 9, no. 8 (2024): 275–82. https://doi.org/10.5281/zenodo.13359418.
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Sarcasm detection is a growing field in Natural Languageg Processing(NLP). Sarcasm is identified by using positive or more positive words, often with a negative connotation, to insult or mock others. In sentiment analysis, detecting sarcasm in the text has become critical. We have reviewed numerous relevant research articles, but due to the Telugu language's limited resources, detecting sarcasm in Telugu texts remains challenging. As a result, the sentiment detection model struggles to accurately identify the exact sentiment of a sarcastic statement, necessitating the development of an automated sarcasm detection system. Many researchers have trained and tested various machine-learning classification algorithms to identify sarcasm, but these algorithms require a dataset as their input, which often contains noise. Various pre-processing techniques are used to remove noise from the dataset. We created a Telugu News Headline dataset on our own and stored in our local machine. Labeled the statements as sarcastic or non-sarcastic by the annotators, and then input them into our proposed model. We built the proposed model using One Hot Encoding(OHE), to transform the dataset into vectors, then fed to the Sarcasm Detection Model to determine the model accuracy. We trained and tested the Sarcasm detection model on positive or even more positive sentences. Resulted accuracy with One Hot Encoding 90.30%. We observed that One Hot Encoding(OHE) had better accuracy on the balanced Telugu news headline dataset.In the future, we can apply more verticle datasets using deep learning algorithms to detect sarcasm for better accuracy.
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Santos, António Paulo, Carlos Ramos, and Nuno C. Marques. "Sentiment Classification of Portuguese News Headlines." International Journal of Software Engineering and Its Applications 9, no. 9 (2015): 9–18. http://dx.doi.org/10.14257/ijseia.2015.9.9.02.
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Rozado, David, Ruth Hughes, and Jamin Halberstadt. "Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models." PLOS ONE 17, no. 10 (2022): e0276367. http://dx.doi.org/10.1371/journal.pone.0276367.
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This work describes a chronological (2000–2019) analysis of sentiment and emotion in 23 million headlines from 47 news media outlets popular in the United States. We use Transformer language models fine-tuned for detection of sentiment (positive, negative) and Ekman’s six basic emotions (anger, disgust, fear, joy, sadness, surprise) plus neutral to automatically label the headlines. Results show an increase of sentiment negativity in headlines across written news media since the year 2000. Headlines from right-leaning news media have been, on average, consistently more negative than headlines from left-leaning outlets over the entire studied time period. The chronological analysis of headlines emotionality shows a growing proportion of headlines denoting anger, fear, disgust and sadness and a decrease in the prevalence of emotionally neutral headlines across the studied outlets over the 2000–2019 interval. The prevalence of headlines denoting anger appears to be higher, on average, in right-leaning news outlets than in left-leaning news media.
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Razaq, Muhammad Umar, and Noor Naeem. "Corpus-Assisted Sentiment Analysis of News Headlines on Palestine-Israel Conflict: A Computational Approach." Global Foreign Policies Review VII, no. IV (2025): 59–67. https://doi.org/10.31703/gfpr.2024(vii-iv).07.
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In this study, corpus-assisted sentiment analysis is used to explore how news headlines frame the Palestine–Israel conflict by analyzing a dataset consisting of 5000 news headlines retrieved from Google News. The sentiment of headlines was classified as positive, negative, or neutral, using a supervised machine learning approach of logistic regression. The sentiment analysis results reveal a dominance of neutral sentiment, with considerable positive sentiment, which is indicative of the resolution-centered framing employed by the media outlets. The study adopts the Media Framing theory and Social Identity theory to establish how media framing impacts public perception and reinforces social identity-based bias. The results highlight the importance of better balanced and solution-oriented reporting in support of conflict resolution efforts. However, the strong presence of negative sentiment, on the contrary, is indicative of media outlets' conflict-oriented framing. Sentiment analysis could be used as a useful tool for policymakers, journalists, and researchers to gauge the effect of media storytelling on public opinion. For future research, multilingual and longitudinal sentiment analysis can be extended to analyze the changing media discourses in a different cultural context.
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Adhi, Ajar Parama, Khairil Umuri, and Gandung Triyono. "SENTIMENT ANALYSIS AND ENTITY DETECTION ON NEWS HEADLINES TO SUPPORT INVESTMENT DECISIONS." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1801–10. https://doi.org/10.52436/1.jutif.2024.5.6.3434.
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Accurate investment decisions are often influenced by information available in the media. News headlines, as part of information media, can provide an initial picture of market sentiment and ongoing trends. This research examines the importance of making appropriate investment decisions with a focus on sentiment analysis and entity detection in news headlines as supporting tools. Through machine learning-based sentiment analysis and Named Entity Recognition (NER) techniques, this study identifies opinions and entities such as company names, stock indices, and industry sectors in news headlines. This research compares three machine learning algorithms, namely SVM, Naive Bayes, and Random Forest using cross-validation. The result shows that the best algorithm is SVM with weighted average F1-score of 76,68%. Furthermore, hyperparameter optimization is performed using Optuna for the SVM algorithm, which is an innovation in the context of sentiment analysis on news headlines in Indonesia. The result shows an increase in weighted average F1-score to 78,14%. For NER, a rule-based method is used by utilizing the Jaro-Winkler string similarity function. The combination of sentiment analysis and NER is then presented in the form of a dashboard using Google Looker Studio tools, with data from sentiment analysis and NER results being processed periodically and automatically using Google Workflows. This research makes a significant contribution by expanding the scope of analysis from just one or a few issuers to all entities published on news portals thanks to NER support, making the results relevant to support investment decisions that are responsive to dynamic market changes.
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Bokare, Dr Madhav M., and Mr Amol V. Suryawanshi. "Using Financial News Headlines to Predict Stock Data and Conduct Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2958–71. https://doi.org/10.22214/ijraset.2025.68455.
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Abstract: This study offers a web application for stock prediction that uses machine learning algorithms to forecast market performance and analyse the sentiment of financial news. LSTM, lexicon-based analysis, and vector-based machine learning approaches are employed. for stock forecasting and sentiment analysis. The sentiment analysis system classifies news headlines as either positive or negative with an accuracy rate of 86%. The mean absolute error of the stock prediction model's stock performance estimation is 3.4 percent. The total accuracy of both models in predicting stock performance is 83%. The results indicate the system's potential for use in the stock market by offering crucial insights into machine learning algorithms for sentiment analysis using financial news headlines and stock data prediction
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Priyatno, Arif Mudi, and Fahmi Iqbal Firmananda. "N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines." RIGGS: Journal of Artificial Intelligence and Digital Business 1, no. 1 (2022): 01–06. http://dx.doi.org/10.31004/riggs.v1i1.4.
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 Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company's reputation. This will affect the company's development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company's financial news. The machine learning methods compared are Multinomial Naïve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis. 
 
 
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Chang, Po-Hsuan, Akshi Kumar, and Saurabh Raj Sangwan. "SENS-HEAD: A Machine Learning Framework for Sensationalism Detection in News Headlines Using Linguistic and Semantic Features." British Journal of Multidisciplinary and Advanced Studies 6, no. 3 (2025): 1–31. https://doi.org/10.37745/bjmas.2022.04909.
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The proliferation of sensationalized news headlines has raised concerns about media integrity, necessitating automated approaches for detecting sensationalism beyond traditional clickbait classification. This study presents SENS-HEAD, a novel dataset comprising over 30,000 annotated headlines labelled for sensational content and emotional arousal. Employing Natural Language Processing (NLP), we extract a diverse set of linguistic and semantic features, including sentiment polarity, syntactic complexity, punctuation distribution, and stop word ratio, to systematically distinguish sensational from non-sensational headlines. We implement ensemble learning models—XGBoost, CATBoost, and Random Forest achieving a balanced F1-score of 0.66. To enhance interpretability, we integrate SHAP (SHapley Additive exPlanations), unveiling key predictive markers such as stop word frequency, headline length, and sentiment extremity. The findings not only advance explainable AI (XAI) for sensationalism detection but also provide practical applications in automated journalism, content moderation, and media ethics regulation. By strengthening computational linguistics with ethical AI, this research delivers actionable insights for policymakers and promotes trustworthy news dissemination in the digital era.
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Khoo, Christopher SG, and Sathik Basha Johnkhan. "Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons." Journal of Information Science 44, no. 4 (2017): 491–511. http://dx.doi.org/10.1177/0165551517703514.
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This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%–77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisation. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorisation task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human-assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts and the WKWSCI lexicon for non-review texts.
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Kamal, Saurabh, Sahil Sharma, Vijay Kumar, Hammam Alshazly, Hany S. Hussein, and Thomas Martinetz. "Trading Stocks Based on Financial News Using Attention Mechanism." Mathematics 10, no. 12 (2022): 2001. http://dx.doi.org/10.3390/math10122001.
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Sentiment analysis of news headlines is an important factor that investors consider when making investing decisions. We claim that the sentiment analysis of financial news headlines impacts stock market values. Hence financial news headline data are collected along with the stock market investment data for a period of time. Using Valence Aware Dictionary and Sentiment Reasoning (VADER) for sentiment analysis, the correlation between the stock market values and sentiments in news headlines is established. In our experiments, the data on stock market prices are collected from Yahoo Finance and Kaggle. Financial news headlines are collected from the Wall Street Journal, Washington Post, and Business-Standard website. To cope with such a massive volume of data and extract useful information, various embedding methods, such as Bag-of-words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), are employed. These are then fed into machine learning models such as Naive Bayes and XGBoost as well as deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Various natural language processing, andmachine and deep learning algorithms are considered in our study to achieve the desired outcomes and to attain superior accuracy than the current state-of-the-art. Our experimental study has shown that CNN (80.86%) and LSTM (84%) are the best performing models in relation to machine learning models, such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%). Moreover, two novel methods, BERT and RoBERTa, were applied with the expectation of better performance than all the other models, and they did exceptionally well by achieving an accuracy of 90% and 88%, respectively.
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Z.U.Kulmatov. "SENTIMENT ANALYSIS OF LATEST NEWS FROM QALAMPIR.UZ (FEBRUARY-MARCH)." Multidisciplinary Journal of Science and Technology 5, no. 3 (2025): 632–35. https://doi.org/10.5281/zenodo.15080910.
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This study presents a sentiment analysis of news articles manually extracted from the English-language section of the Qalampir.uz website, covering the period from February to March. By applying natural language processing techniques, the study classifies the sentiment of news headlines into three categories: positive, negative, and neutral. The findings indicate that the majority of news content is neutral, with a notable presence of both positive and negative news items. These results provide insights into the tone and focus of recent media coverage in Uzbekistan and contribute to the broader field of computational journalism (Pang & Lee, 2008).
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Muhammad, Iftikhar, and Marco Rospocher. "On Assessing the Performance of LLMs for Target-Level Sentiment Analysis in Financial News Headlines." Algorithms 18, no. 1 (2025): 46. https://doi.org/10.3390/a18010046.
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The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain.
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Abdussalam, Muhammad Faris, Donni Richasdy, and Moch Arif Bijaksana. "BERT Implementation on News Sentiment Analysis and Analysis Benefits on Branding." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 4 (2022): 2064. http://dx.doi.org/10.30865/mib.v6i4.4579.
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The rapid development of information makes data processing easy and fast, especially in the business world, so many business brands have used the internet as a marketing medium for their operations. Now the business does not only depend on its operations; now, the opinion of the public media, especially on the news, has become an essential spotlight in today's business, especially against negative opinions that indirectly impact the image and product branding of the business, we need the proper means to help identifying and analyzing this kind of news. This study aims to identify and analyze sentiment with negative and positive indications on news titles from one of the sources of an Indonesian online news portal using the Bidirectional Representations from Transformers (BERT) sentiment analysis method, with the measurement of the confusion matrix metrics to measure and identify which headlines contains negative and positive indications. The sentiment analysis system offers identification and categorization with ease and immediately provide good results on identifying news. The results of this study, the sentiment model achieves an accuracy rate of 93% in identifying negative and positive news and F1-Score on negative identification rate of 92% and positive identification rate of 93%. The sentiment analysis system was built as effort to help analyzing against positive news indications or awful news as analysis benefits carried out to identifying alarming news indications towards branding.
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Martinez-Araneda, Claudia, Alejandra Segura, Christian Vidal-Castro, and Jorge Elgueta. "Is news really pessimistic? Sentiment Analysis ofChilean online newspaper headlines." Indian Journal of Science and Technology 11, no. 22 (2018): 1–8. http://dx.doi.org/10.17485/ijst/2018/v11i20/102251.
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Martinez-Araneda, Claudia, Alejandra Segura, Christian Vidal-Castro, and Jorge Elgueta. "Is news really pessimistic? Sentiment Analysis ofChilean online newspaper headlines." Indian Journal of Science and Technology 11, no. 22 (2018): 1–8. http://dx.doi.org/10.17485/ijst/2018/v11i22/102251.
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Wen, Chenfeiyu, Xiangting Wu, Chuyue Shen, Zifei Huang, and Peiqi Cai. "Bitcoin price prediction based on sentiment analysis and LSTM." Applied and Computational Engineering 29, no. 1 (2023): 148–59. http://dx.doi.org/10.54254/2755-2721/29/20230743.
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As cryptocurrencies become widely accepted due to technical improvements, reliable approaches to capture their future price movements of them become critical. This study mainly combines weighted sentiment analysis results from social media-related comments and financial news headlines with a stacked LSTM model to predict second-day Bitcoin price evolution. This study also compared our results and the results produced by MLP, RF, and SVM after feeding the sentiment analysis results.
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Sai, Kalyana Pranitha Buddiga. "Deep Learning Approach to Sentiment Analysis in Financial Markets: Algorithms Overview." European Journal of Advances in Engineering and Technology 10, no. 8 (2023): 47–49. https://doi.org/10.5281/zenodo.11213753.
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Financial news headlines play a crucial role in shaping market sentiment and influencing investor decisions. This paper provides an overview of deep learning approach to sentiment analysis in financial markets, focusing on algorithms and applications. It discusses the importance of sentiment analysis in understanding market dynamics and investor behavior, highlighting the limitations of traditional machine learning techniques. The paper explores transformer-based models like BERT (Bidirectional Encoder Representations from Transformers), FinBERT and highlights the ability of deep learning models to capture nuanced sentiment signals and their potential applications in algorithmic trading, risk management, and investor sentiment analysis.
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Li, Wang, Chaozhu Hu, and Youxi Luo. "A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment." Electronics 12, no. 18 (2023): 3960. http://dx.doi.org/10.3390/electronics12183960.
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Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns.
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Changyue Li. "Computer-Assisted Analysis of Qualitative News Dissemination." Journal of Electrical Systems 20, no. 6s (2024): 217–25. http://dx.doi.org/10.52783/jes.2631.
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This study delves into the potential of utilizing deep learning (DL) techniques to analyze qualitative news dissemination for trading purposes. DL, renowned for its prowess in handling vast datasets and deciphering intricate patterns, holds promise in aiding investors seeking to enhance their trading strategies. Specifically, Long Short-Term Memory (LSTM) networks, known for their capacity to retain contextual information, are explored in this research. By employing DL models, we aim to forecast market sentiment based on news headlines, focusing on the Dow Jones industrial average from 2008 to 2020. Leveraging 25 daily news headlines, we extend our analysis to develop an algorithmic trading strategy. Through rigorous testing across two distinct cases over five-time steps, our study evaluates the effectiveness of DL-driven approaches in real-world trading scenarios. Furthermore, this research contributes to the growing body of literature on the intersection of deep learning and financial markets. By examining the application of DL in qualitative news analysis for trading purposes, we provide insights into the potential implications for investors and financial analysts. The findings offer valuable guidance for leveraging advanced computational techniques for decision-making in dynamic market environments. Additionally, this study underscores the importance of incorporating qualitative news data into trading strategies, highlighting the role of DL in extracting meaningful signals from unstructured textual information’s Overall, our findings shed light on the opportunities and challenges associated with harnessing DL for trading on news sentiment.
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Lee, Jueun, and Tae Yeon Kwon. "The Korean News Financial Sentiment Index(KNFSI) and Its Relationship with the KOSPI 200." Korean Data Analysis Society 27, no. 2 (2025): 521–34. https://doi.org/10.37727/jkdas.2025.27.2.521.
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The stock market is closely associated with news. However, since the interpretation of news can be subjective, and conventional stock market analysis primarily relies on structured data and quantitative variables, the incorporation of news information remains limited. This paper introduces the Korean News Financial Sentiment Index(KNFSI), a new financial sentiment index tailored to the Korean market, and investigates its relationship with KOSPI 200 returns. To create this index, U.S.-based financial keywords were adapted to reflect the Korean market, and KR-FinBERT, a sentiment analysis model for Korean financial texts, was applied to news headlines from January 1 to December 31, 2023. The KNFSI consists of the composite index and two sub-indices: KNFSI⁻(reflecting negative sentiment) and KNFSI⁺ (reflecting positive sentiment). Empirical results show that KNFSI is positively correlated with KOSPI 200 returns, with KNFSI⁻ showing strong explanatory power during market downturns. In addition, both simple and multiple linear regression models confirm that KNFSIt and KNFSIt⁻ are significant explanatory variables for stock returns.
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Baraniak, Katarzyna, and Marcin Sydow. "A dataset for Sentiment analysis of Entities in News headlines (SEN)." Procedia Computer Science 192 (2021): 3627–36. http://dx.doi.org/10.1016/j.procs.2021.09.136.
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Khonde, Shraddha R., Shyamal S. Virnodkar, Sangita B. Nemade, Manisha A. Dudhedia, Bhavana Kanawade, and Shravan H. Gawande. "Sentiment Analysis and Stock Data Prediction Using Financial News Headlines Approach." Revue d'Intelligence Artificielle 38, no. 3 (2024): 999–1008. http://dx.doi.org/10.18280/ria.380325.
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Xu, Yingjie. "Critical Discourse Analysis of People’s Daily and the New York Times’ Headlines on the Beijing Winter Olympics." New Horizons in English Studies 9 (December 30, 2024): 16–42. https://doi.org/10.17951/nh.2024.9.16-42.
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This paper examined the headlines of the Chinese newspaper, People’s Daily, and the American newspaper, the New York Times, about the Beijing Winter Olympics in 2022. To investigate the possible ideological biases of news headlines, this paper performs corpus-based critical discourse analysis. It hypothesizes that Chinese media will use positive strategies to report the event, whereas American media will maintain a neutral stance. Fairclough’s three-dimensional analysis model and sentiment analysis tools via Google apps were applied to analyze news headlines of the two newspapers. With “Beijing Winter Olympics” as the keyword, People’s Daily generated 261 results and a sentiment score of 60.2 whereas the New York Times produced 235 results and a sentiment score of 0.1 from February 3 to 20, 2022. The analysis reveals that People’s Daily predominantly uses positive language and active voice, frequently quoting authoritative figures to reinforce the success and international approval of the Beijing Winter Olympics. Notable features include a high frequency of positive words, reinforce China’s high-power position in the sentence structure. Conversely, the New York Times presents a more balanced perspective, with both positive and negative sentiments. It often employs passive voice and indirect language to highlight controversies and challenges, such as human rights issues and the political implications of the Games. The New York Times’ headlines also exhibit the use of metaphors and euphemisms to subtly convey skepticism about China’s intentions. The conclusion of this study is that People’s Daily is committed to establishing and maintaining the image of a strong and capable Chinese government, while the New York Times, although striving for journalistic objectivity, still exposes ideological differences towards China, which also reflects the geopolitical tensions between China and the United States.
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Wadhwa, Shalini, and Ananya Shukla. "Sentiment analysis and word cloud analysis on e-waste news headlines using Python." International Journal of Environment and Waste Management 37, no. 2 (2025): 201–14. https://doi.org/10.1504/ijewm.2025.146556.
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Heru, Agus Santoso, Hari Rachmawanto Eko, Nugraha Adhitya, Aji Nugroho Akbar, Rosal Ignatius Moses Setiadi De, and Suko Basuki Ruri. "Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 799–806. https://doi.org/10.12928/TELKOMNIKA.v18i2.14744.
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Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
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Nemes, László, and Attila Kiss. "Prediction of stock values changes using sentiment analysis of stock news headlines." Journal of Information and Telecommunication 5, no. 3 (2021): 375–94. http://dx.doi.org/10.1080/24751839.2021.1874252.
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Lekkas, Damien, Joseph A. Gyorda, George D. Price, Zoe Wortzman, and Nicholas C. Jacobson. "Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect." Journal of Medical Internet Research 24, no. 1 (2022): e32731. http://dx.doi.org/10.2196/32731.
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Background The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods Using COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.
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Ranathunga, Surangika, and Isuru Udara Liyanage. "Sentiment Analysis of Sinhala News Comments." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (2021): 1–23. http://dx.doi.org/10.1145/3445035.
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Sinhala is a low-resource language, for which basic language and linguistic tools have not been properly defined. This affects the development of NLP-based end-user applications for Sinhala. Thus, when implementing NLP tools such as sentiment analyzers, we have to rely only on language-independent techniques. This article presents the use of such language-independent techniques in implementing a sentiment analysis system for Sinhala news comments. We demonstrate that for low-resource languages such as Sinhala, the use of recently introduced word embedding models as semantic features can compensate for the lack of well-developed language-specific linguistic or language resources, and text classification with acceptable accuracy is indeed possible using both traditional statistical classifiers and Deep Learning models. The developed classification models, a corpus of 8.9 million tokens extracted from Sinhala news articles and user comments, and Sinhala Word2Vec and fastText word embedding models are now available for public use; 9,048 news comments annotated with POSITIVE/NEGATIVE/NEUTRAL polarities have also been released.
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Cardoso Durier da Silva, Fernando, Ana Cristina Bicharra Garcia, and Sean Wolfgand Matsui Siqueira. "Sentiment Gradient - Improving Sentiment Analysis with Entropy Increase." Inteligencia Artificial 26, no. 71 (2023): 114–30. http://dx.doi.org/10.4114/intartif.vol26iss71pp114-130.
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Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similarwritten styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94 %, with our approach surpassing available ones (with a p-value less than 0.05 for our results).
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Liu, Bo, and Natalia Vladimirovna Perfilieva. "Rhizomatic Nature of Digital Media News Headlines in Russian and Chinese." Litera, no. 1 (January 2025): 41–50. https://doi.org/10.25136/2409-8698.2025.1.72972.
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The article is devoted to identifying common patterns of rhizomatic news headlines in Russian and Chinese leading online media. The subject of the study is the rhizomatic nature of digital media news headlines, which is manifested in the complex interrelationships of texts in cyberspace. The material is the headlines of international news from leading Russian and Chinese digital media for 2024. The scientific novelty is determined by the fact that for the first time an attempt has been made to conduct a comparative study on the main manifestations of the rhizomatic nature of news headlines in Russian and Chinese leading digital media. Being an announcement and hyperlink of an Online media news article, complex digital headline texts receive typical rhizomatic characteristics such as: interconnectedness, heterogeneity, multiplicity, nonlinearity, openness, dynamism, the status of which is not clearly defined. Their analysis determines the relevance of the study. The research methods combine the classification of linguistic units, linguistic description, functional and semantic analysis, and comparison to identify common rhizomatic properties of news headlines from leading Russian and Chinese online media. The results of the study show that rhizomaticity is characteristic of the international news headlines of Russian and Chinese leading digital media. An analysis of the complex of news headlines in Russian and Chinese digital media based on 6 main characteristics of the rhizome showed that rhizomatic relationships are found in news headline texts, which exhibit the following properties: citation, hypertextuality, intertextuality, precedent, heterogeneity, multidimensionality, multiplicity, discreteness, fragmentation, out-of-structure, nonlinearity, deterritorialization, reterritorialization, decentralization openness and dynamism. The obtained research results can be the basis for further research in this field, and used in the practice of teaching university courses in relevant disciplines. The conclusion of the study is that rhizomaticity is the main attribute of the texts of news headlines in Russian and Chinese leading digital media.
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Nkongolo Wa Nkongolo, Mike. "News Classification and Categorization with Smart Function Sentiment Analysis." International Journal of Intelligent Systems 2023 (November 13, 2023): 1–24. http://dx.doi.org/10.1155/2023/1784394.
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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.
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Pelicon, Andraž, Marko Pranjić, Dragana Miljković, Blaž Škrlj, and Senja Pollak. "Zero-Shot Learning for Cross-Lingual News Sentiment Classification." Applied Sciences 10, no. 17 (2020): 5993. http://dx.doi.org/10.3390/app10175993.
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In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.