Relevant bibliographies by topics / Sentiment classification News Headlines analysis

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Sentiment classification News Headlines analysis'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Sentiment classification News Headlines analysis"

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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
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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
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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 perfo
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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 senti
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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
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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 vecto
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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 o
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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
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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 sentim
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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 vect
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Dissertations / Theses on the topic "Sentiment classification News Headlines analysis"

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Michel, David. "All Negative on the Western Front: Analyzing the Sentiment of the Russian News Coverage of Sweden with Generic and Domain-Specific Multinomial Naive Bayes and Support Vector Machines Classifiers." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447398.

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This thesis explores to what extent Multinomial Naive Bayes (MNB) and Support Vector Machines (SVM) classifiers can be used to determine the polarity of news, specifically the news coverage of Sweden by the Russian state-funded news outlets RT and Sputnik. Three experiments are conducted.  In the first experiment, an MNB and an SVM classifier are trained with the Large Movie Review Dataset (Maas et al., 2011) with a varying number of samples to determine how training data size affects classifier performance.  In the second experiment, the classifiers are trained with 300 positive, negative, an
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OU, MENG-SHAIN, and 歐孟賢. "The Study of Sentiment Analysis on the Influence of News Headlines on Audiences." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/25573591699639322265.

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碩士<br>國立臺南大學<br>數位學習科技學系碩博士班<br>104<br>News headlines not only show the summary of context, but the most importantly attract audiences’ interest. Especially the information on internet flow fast and there are many event taking place, so that produce lot of network news. Normal news’ headlines obviously can’t catch audiences’ eyes, instead of, there are more and more sensational or suspenseful headlines with emotion evoked in order to add audiences’ interest. This study used crawler to catch May headlines of a popular news website, and the totally number were 4000 about news, and used Jieba to
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Elhaddad, Mohamed Kamel Abdelsalam. "Web mining for social network analysis." Thesis, 2021. http://hdl.handle.net/1828/13219.

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Undoubtedly, the rapid development of information systems and the widespread use of electronic means and social networks have played a significant role in accelerating the pace of events worldwide, such as, in the 2012 Gaza conflict (the 8-day war), in the pro-secessionist rebellion in the 2013-2014 conflict in Eastern Ukraine, in the 2016 US Presidential elections, and in conjunction with the COVID-19 outbreak pandemic since the beginning of 2020. As the number of daily shared data grows quickly on various social networking platforms in different languages, techniques to carry out automatic c
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Book chapters on the topic "Sentiment classification News Headlines analysis"

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Kabra, Ashwin, and Seema Shrawne. "Location-Wise News Headlines Classification and Sentiment Analysis: A Deep Learning Approach." In International Conference on Intelligent Computing and Smart Communication 2019. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_37.

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Roy, Manish Chandra, Sukant Kishoro Bisoy, and Pradipta Kumar Das. "News Headlines Sentiment Analysis Using Vectorization Techniques." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4496-1_16.

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Bhavsar, Hetal, Anjali Jivani, Sameer Amesara, Smeet Shah, Prashant Gindani, and Sohamkumar Patel. "Stock Price Prediction Using Sentiment Analysis on News Headlines." In ICT with Intelligent Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3571-8_4.

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Jain, Janhavi, Debadrita Dey, Bhavika Kelkar, and Khyati Ahlawat. "Analysis of Indian News with Corona Headlines Classification." In Artificial Intelligence and Speech Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95711-7_10.

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Atzeni, Mattia, Amna Dridi, and Diego Reforgiato Recupero. "Fine-Grained Sentiment Analysis on Financial Microblogs and News Headlines." In Semantic Web Challenges. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69146-6_11.

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Shuhidan, Shuhaida Mohamed, Saidatul Rahah Hamidi, Soheil Kazemian, Shamila Mohamed Shuhidan, and Maizatul Akmar Ismail. "Sentiment Analysis for Financial News Headlines using Machine Learning Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8612-0_8.

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Saxena, Aastha, Arpit Jain, Prateek Sharma, Sparsh Singla, and Amrita Ticku. "Sentiment Analysis of Stocks Based on News Headlines Using NLP." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6239-266-3_12.

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Saxena, Aastha, Arpit Jain, Prateek Sharma, Sparsh Singla, and Amrita Ticku. "Sentiment Analysis of Stocks Based on News Headlines Using NLP." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-074-9_12.

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Hamborg, Felix. "Frame Analysis." In Revealing Media Bias in News Articles. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17693-7_5.

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AbstractThis chapter details the last component of person-oriented framing analysis: frame analysis. The component aims to classify how persons are portrayed in news articles. The chapter introduces and discusses two approaches for this task. First, it briefly presents an exploratory approach that aims to classify fine-grained categories of how persons are portrayed. Afterward, the chapter introduces the first method for target-dependent sentiment classification in the domain of news articles. The dataset and method enable sentiment classification in a domain that could not reliably be analyzed earlier. Lastly, the chapter argues for using the latter approach in the frame analysis component, in particular because of its high classification performance.
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Limone, Ankit, Mahak Gupta, Nitin Nagar, and Shaligram Prajapat. "Effect of Financial News Headlines on Crypto Prices Using Sentiment Analysis." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47508-5_17.

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Conference papers on the topic "Sentiment classification News Headlines analysis"

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Singh, Anurag, and Mahalakshmi P. "Stochastic Sentiment Analysis for Trading Signals Based on Financial News Headlines." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739089.

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Juroš, Jana, Laura Majer, and Jan Snajder. "LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma." In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wassa-1.27.

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Gao, Zhoutian, and Ting Wang. "A study of automatic classification of news headlines by bidirectional GRU models." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037938.

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Berta, Matteo, Salvatore Greco, Giuseppe Tipaldo, and Tania Cerquitelli. "Decoding Narratives: Towards a Classification Analysis for Stereotypical Patterns in Italian News Headlines." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825258.

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Madala, Uuhasri, Soumya Puvvada, and Deepak Chowdary. "Revolutionizing News Genre Classification with a Novel Hybrid Sentiment Analysis Model." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10863992.

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Menon, Vishnu Achutha, Sateesh Kumar T K, Juby Thomas, and Lijo P. Thomas. "Sentiment Analysis of Translated BBC Hindi News Articles Using Machine Learning: A Comparative Study of Classification Algorithms." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866492.

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Singh, Anurag, and Goonjan Jain. "Sentiment Analysis of News Headlines Using Simple Transformers." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544806.

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Singh, Anurag, and Goonjan Jain. "Sentiment Analysis of News Headlines Using Simple Transformers." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544806.

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Kaliappan, S., L. Natrayan, and Akshay Rajput. "Sentiment Analysis of News Headlines Based on Sentiment Lexicon and Deep Learning." In 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2023. http://dx.doi.org/10.1109/icosec58147.2023.10276102.

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Soni, Jitendra, and Kirti Mathur. "Sentiment Analysis of News Headlines for Stock Market Prediction using VADER." In 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2023. http://dx.doi.org/10.1109/icimia60377.2023.10426095.

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