Relevant bibliographies by topics / Opinion or Sentiment Mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Opinion or Sentiment Mining'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Opinion or Sentiment Mining"

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Datt, Jivat Singh. "SENTIMENT ANALYSIS USING CUSTOMER FEEDBACK." International Journal of Trendy Research in Engineering and Technology 07, no. 04 (2023): 09–13. http://dx.doi.org/10.54473/ijtret.2023.7402.

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This Sentiment analysis is one of the fastest spreading research areas in computer science, making it challenging to keep track of all the activities in the area. We present customer feedback reviews on products, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product. Data used in this study are online product reviews collected from Amazon.com. We performed a comparative sentiment analysis of retrieved reviews. This research paper provides you with sentimental analysis of various smart phone opinions
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Kumar, Ravindra. "Methods to Perform Opinion Mining and Sentiment Analysis to Detect Factors Affecting Mental Health." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 70–72. http://dx.doi.org/10.35940/ijeat.f3025.1011121.

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Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is crucial to
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Ravindra, Kumar. "Methods to Perform Opinion Mining and Sentiment Analysis to Detect Factors Affecting Mental Health." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 70–72. https://doi.org/10.35940/ijeat.F3025.1011121.

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Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is cruc
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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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Ramandeep, Sandhu, and Jyoti Kiran. "A NEW METHOD TO FIND SCORE VALUE FOR ONLINE OPINIONS." International Journal of Computational Science and Information Technology (IJCSITY) 1, February (2013): 1–9. https://doi.org/10.5281/zenodo.3631132.

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<strong>ABSTRACT </strong> In today&rsquo;s world, we need information, not data to take a decision about a product. Opinions are data and become information after filtering. Also organizations products superiority is dependent on customer feedback of their products. Web is a platform providing facilities to post opinions for any product. A customer gives feedback when he uses a product and feedback is what customer feels after using it called sentiments/opinions. Opinions are subjective sentences and not objective as facts. People uses natural language to express sentiments and way of express
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Purohit, Amit. "Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 233–39. http://dx.doi.org/10.22214/ijraset.2021.36202.

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Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express e
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Bhardwaj, Shubham. "AN INDEPTH ANALYSIS OF CATEGORIZED MINING ALGORITHMS FOR OPINION MINING." International Journal of Research in Science and Technology 10, no. 01 (2022): 53–57. http://dx.doi.org/10.37648/ijrst.v10i01.011.

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Today's information and ideas can't be shared without social media. A person's day-to-day life is significantly affected by their emotional impact. An ecosystem that generates millions of bytes of data daily makes sentiment analysis essential for interpreting these enormous amounts of data. Sentiment analysis, a type of text mining, finds and extracts personal information from various sources, allowing businesses to monitor social sentiment about their brand, product, or service. Simply put, sentiment analysis enables one to ascertain the author's perspective on a topic. Writing is categorized
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Abubakar, B. U., and C. Uppin. "A NATURAL LANGUAGE PROCESSING APPROACH TO DETERMINE THE POLARITY AND SUBJECTIVITY OF IPHONE 12 TWITTER FEEDS USING TEXTBLOB." Open Journal of Physical Science (ISSN: 2734-2123) 2, no. 2 (2021): 10–17. http://dx.doi.org/10.52417/ojps.v2i2.276.

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Sentiment analysis and opinion mining is a branch of computer science that has gained considerable growth over the last decade. This branch of computer science deals with determining the emotions, opinions, feelings amongst others of a person on a particular topic. Social media has become an outlet for people to voice out their thoughts and opinions publicly about various topics of discussion making it a great domain to apply sentiment analysis and opinion mining. Sentiment analysis and opinion mining employ Natural Language Processing (NLP) in order to fairly obtain the mood of a person’s opi
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Miss, Komal, Devendra Kumar Vashist Er., and Balram Bhardwaj Er. "A NAIVE-BAYES STRATEGY FOR SENTIMENT ANALYSIS FOR E –CURRIER : FAST AND ACCURATE SENTIMENT CLASSIFICATION." International Journal of Advances in Engineering & Scientific Research 3, no. 2 (2024): 44–58. https://doi.org/10.5281/zenodo.10750567.

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<strong>Abstract: </strong> &nbsp; <em>The amount of data readily available is far beyond our capacity to analyse and understand. The internet revolution has added to this problem by having billions of customer&rsquo;s review data in its repositories. This has provoked an interest in sentiment analysis and opinion mining in the recent years. Opinion Mining is a process of automatic extraction of knowledge by means of opinion of others about some particular product, topic or problem. The idea of Opinion mining and Sentiment Analysis tool is to process a set of search results for a given item ba
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Vishwakarma, Shweta. "A Review Paper on Sentiment Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 528–31. http://dx.doi.org/10.22214/ijraset.2023.56545.

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Abstract: Opinion Mining (OM) or Sentiment Analysis (SA) can be described as the process of identifying, extracting, and categorizing viewpoints on various subjects. It falls under the domain of natural language processing (NLP) and is commonly employed to gauge public sentiment towards specific laws, policies, marketing campaigns, and more. This involves the development of methodologies to collect and analyze comments and opinions posted on social media platforms concerning legislation, regulations, and other related matters. Information extraction plays a pivotal role in this process, as it
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Dissertations / Theses on the topic "Opinion or Sentiment Mining"

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Kavaliauskas, Vytautas. "Nuomonių analizės taikymas komentarams lietuvių kalboje." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110615_130252-94422.

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Pastaruosius keletą metų, žmonėms vis aktyviau pradėjus reikšti savo požiūrį, įsitikinimus ir potyrius internete, susiformavo nauja tyrinėjimų sritis, kuri apima nuomonių gavybą ir sentimentų analizę. Šios srities tyrinėjimus aktyviai skatina ir jais domisi įvairios verslo kompanijos, matančios didelį, dėka nuolat tobulėjančių rezultatų, praktinį potencialą. Šis darbas skirtas apžvelgti teorinius bei praktinius nuomonės gavybos ir sentimentų analizės rezultatus bei realizuoti prototipinę nuomonės analizės sistemą, skirtą tyrinėti trumpus komentarus, parašytus lietuvių kalba. Taip pat darbe apr
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Li, Hanzhe. "Sentiment Analysis and Opinion Mining on Twitter with GMO Keyword." Thesis, North Dakota State University, 2016. http://hdl.handle.net/10365/25787.

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Twitter are a new source of information for data mining techniques. Messages posted through Twitter provide a major information source to gauge public sentiment on topics ranging from politics to fashion trends. The purpose of this paper is to analyze the Twitter tweets to discern the opinions of users regarding Genetically Modified Organisms (GMOs). We examine the effectiveness of several classifiers, Multinomial Na?ve Bayes, Bernoulli Na?ve Bayes, Logistic Regression and Linear Support Vector Classifier (SVC) in identifying a positive, negative or neutral category on a tweet corpus. Addition
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ROMANO, MAURIZIO. "General Sentiment Decomposition: opinion mining based on raw Natural Language text." Doctoral thesis, Università degli Studi di Cagliari, 2021. http://hdl.handle.net/11584/313197.

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The importance of person-to-person communication about a certain topic (Word of Mouth) is growing day by day, especially for decision-makers. These phenomena can be directly observed in online social networks. For example, the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. Forby, those people usually leave a review for it. Such a review will directly impact the product, and this effect is amplified proportionally to how much the reviewer is considered to be trustworthy by the potential new custo
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Yaakub, Mohd Ridzwan. "Integration of Opinion Mining into customer analysis model." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/85084/1/Mohd%20Ridzwan_Yaakub_Thesis.pdf.

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This research proposes a multi-dimensional model for Opinion Mining, which integrates customers' characteristics and their opinions about products (or services). Customer opinions are valuable for companies to deliver right products or services to their customers. This research presents a comprehensive framework to evaluate opinions' orientation based on products' hierarchy attributes. It also provides an alternative way to obtain opinion summaries for different groups of customers and different categories of produces.
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Melloncelli, Damiano. "Sentiment analysis in Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6592/.

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Gli ultimi anni hanno visto una crescita esponenziale nell’uso dei social media (recensioni, forum, discussioni, blog e social network); le persone e le aziende utilizzano sempre più le informazioni (opinioni e preferenze) pubblicate in questi mezzi per il loro processo decisionale. Tuttavia, il monitoraggio e la ricerca di opinioni sul Web da parte di un utente o azienda risulta essere un problema molto arduo a causa della proliferazione di migliaia di siti; in più ogni sito contiene un enorme volume di testo non sempre decifrabile in maniera ottimale (pensiamo ai lunghi messaggi di forum e
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Haider, Syed Zeeshan. "AN ONTOLOGY BASED SENTIMENT ANALYSIS : A Case Study." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-6387.

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Business through e-commerce has become popular recently due to the massive amount of information available on internet. This has resulted in the abnormal number of reviews on websites like www.amazon.com  and www.ebay.com, where customers express their opinions about the purchases they have made. Analyzing customer’s behavior has become very important for the organizations to find new market trends and insights. For the potential customer  it becomes really difficult to get the knowledge about a product in the presence of such huge number of reviews and to sort the useful reviews and make good
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Guardati, Simone. "Realizzazione di una infrastruttura di Opinion Mining per commenti testuali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Milioni di commenti ogni giorno vengono scritti ed aggiunti su internet da migliaia di utenti, essi riguardano ristoranti, hotel, acquisti, luoghi da visitare e molto altro. Quotidianamente moltissime persone fano affidamento su tali opinioni, ad esempio preferendo l’acquisto di un prodotto rispetto ad un altro su un sito di e-commerce, rendendo così il valore di questi dati molto elevato ed una loro analisi un processo molto importante da effettuare. Estrarre le informazioni di interesse da essi è un procedimento difficilmente eseguibile da operatori umani data la loro enorme quantità, di con
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Beltrani, Claudia. "Studio ed analisi di tecniche di opinion mining per contesti di promozione turistica su piattaforme di social media." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8382/.

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Negli ultimi anni Internet ha cambiato le modalità di creazione e distribuzione delle informazioni turistiche. Un ruolo fondamentale viene ricoperto dalle piattaforme di social media, tecnologie che permettono ai consumatori di condividere le proprie esperienze ed opinioni. Diventa necessario, quindi, comprendere i cambiamenti in queste tecnologie e nel comportamento dei viaggiatori per poter applicare strategie di marketing di successo. In questo studio, utilizzando Opinion Finder, un software spesso impiegato nel campo dell'opinion mining, si esamineranno da un punto di vista qualitativo i
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Alhazmi, Samah. "Linking Arabic social media based on similarity and sentiment." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/linking-arabic-social-media-based-on-similarity-and-sentiment(04288028-707c-46f2-a028-8ee3066dfa89).html.

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A large proportion of World Wide Web (WWW) users treat it as a social medium, i.e. many of them use the WWW to express and communicate their opinions. Economic value or utility can be created if these utterances, reactions, or feedback are extracted from various social media platforms and their content analysed. Some of these benefits are related to e-commerce, marketing, product improvements, improving machine learning algorithms etc. Moreover, establishing links between different social media platforms, based on shared topics and content, could provide access to the comments of users of diff
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Duarte, Eduardo Santos. "Sentiment analysis on twitter for the portuguese language." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11338.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>With the growth and popularity of the internet and more specifically of social networks, users can more easily share their thoughts, insights and experiences with others. Messages shared via social networks provide useful information for several applications, such as monitoring specific targets for sentiment or comparing the public sentiment on several targets, avoiding the traditional marketing research method with the use of surveys to explicitly get the public opinion. To extract information from the large amounts o
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Books on the topic "Opinion or Sentiment Mining"

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Liu, Bing. Sentiment Analysis and Opinion Mining. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02145-9.

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Pang, Bo. Opinion mining and sentiment analysis. Now Publishers, 2008.

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Chen, Chung-Chi, Hen-Hsen Huang, and Hsin-Hsi Chen. From Opinion Mining to Financial Argument Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2881-8.

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Bhatia, Surbhi, Poonam Chaudhary, and Nilanjan Dey. Opinion Mining in Information Retrieval. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5043-0.

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Petz, Gerald. Opinion Mining im Web 2.0. Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-23801-8.

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Sweta, Soni. Sentiment Analysis and its Application in Educational Data Mining. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2474-1.

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Singer, David. Probing public sentiment on Israel and American Jews: The February 1987 Roper poll. American Jewish Committee, Institute of Human Relations, 1987.

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Smith, Duane A. Mining America: The industry and the environment, 1800-1980. University Press of Colorado, 1993.

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Danneman, Nathan. Social media mining with R: Deploy cutting-edge sentiment analysis techniques to real-world social media data R. Packt Pub., 2014.

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Bukey, Evan Burr. Hitler's Austria: Popular sentiment in the Nazi era, 1938-1945. University of North Carolina Press, 2000.

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Book chapters on the topic "Opinion or Sentiment Mining"

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Sonntag, Jonathan, and Manfred Stede. "Sentiment Analysis: What’s Your Opinion?" In Text Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12655-5_9.

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Liu, Bing. "Sentiment Lexicon Generation." In Sentiment Analysis and Opinion Mining. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02145-9_6.

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Liu, Bing. "Document Sentiment Classification." In Sentiment Analysis and Opinion Mining. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02145-9_3.

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Liu, Bing. "Opinion Mining and Sentiment Analysis." In Web Data Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_11.

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Zong, Chengqing, Rui Xia, and Jiajun Zhang. "Sentiment Analysis and Opinion Mining." In Text Data Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0100-2_8.

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Liu, Bing. "Opinion Search and Retrieval." In Sentiment Analysis and Opinion Mining. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02145-9_9.

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Liu, Bing. "Aspect-based Sentiment Analysis." In Sentiment Analysis and Opinion Mining. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02145-9_5.

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Zhang, Lei, and Bing Liu. "Sentiment Analysis and Opinion Mining." In Encyclopedia of Machine Learning and Data Science. Springer US, 2023. http://dx.doi.org/10.1007/978-1-4899-7502-7_907-2.

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Aggarwal, Charu C. "Opinion Mining and Sentiment Analysis." In Machine Learning for Text. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73531-3_13.

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Zhang, Lei, and Bing Liu. "Sentiment Analysis and Opinion Mining." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_907-1.

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Conference papers on the topic "Opinion or Sentiment Mining"

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Gurav, Vaishali, and Sanjay Bang. "Opinion Mining and Sentiment Analysis in Legal AI Research." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911556.

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K, Srikanth Bhat, Mallesh Sajjan B. N, Mohammed Hasan Raza, N. Hemanth Bhat, and Naveen B. "Opinion Mining for Comment Sentiment Analysis of Social Media." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859937.

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R M, Dilip Charaan, Vimala Ithayan J, Sankar M, R. Chithambaramani, Sivaprakash P, and D. Marichamy. "Sentiment Analysis and Opinion Mining on Social Media Using Machine Learning." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696144.

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Abrar, Mohammad, Laila Abd-Ellatif Abd-Elmegid, and Alaa A. K. Ismaeel. "Hybrid Sentiment Analysis Model for Outlier Opinion Mining in Cross-Domain Applications." In 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2024. http://dx.doi.org/10.1109/m2vip62491.2024.10746051.

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R, Sathya Janaki, and Sridevi PC. "Real-Time Sentiment Mining: Aspect-Based and Implicit Opinion Extraction in Social Media Posts." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894531.

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B, Vybhavi, Karthika S, Ramy Read Hossain, Amit Chaudhary, and Vadivel R. "Sentiment Analysis and Opinion Mining using Decoding Enhanced BERT with Disentangled Attention and Light Gradient Boosting Machine." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11071244.

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Zhang, Qi, Yuanbin Wu, Yan Wu, and Xuanjing Huang. "Opinion Mining with Sentiment Graph." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.12.

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Chen, Jian, Yu Liu, Guangyi Zhang, Yi Cai, Tao Wang, and Huaqing Min. "Sentiment Analysis for Cantonese Opinion Mining." In 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies (EIDWT). IEEE, 2013. http://dx.doi.org/10.1109/eidwt.2013.89.

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Molavi, Homa, and Lihong Zhang. "A Bibliometric Study of Stakeholder Opinion Mining and Sentiment Analysis in Crisis Communication." In CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics. Universitat Politècnica de València, 2024. http://dx.doi.org/10.4995/carma2024.2024.17782.

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In the contemporary landscape, the ability to effectively manage crises and communicate with stakeholders is paramount for organizations. As the frequency and complexity of crises continue to escalate, understanding stakeholder opinions and sentiments becomes increasingly crucial for crafting timely and appropriate responses. This bibliometric study delves into the landscape of stakeholder opinion mining and sentiment analysis within crisis communication, aiming to discern trends, identify key contributors, and uncover potential gaps in the existing literature. Leveraging data from the Scopus
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Gelbukh, Alexander. "Sentiment analysis and opinion mining: Keynote address." In 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2017. http://dx.doi.org/10.1109/icrito.2017.8342396.

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Reports on the topic "Opinion or Sentiment Mining"

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Vizmanos, Jana Flor, Sheila Siar, Jose Ramon Albert, Janina Luz Sarmiento, and Angelo Hernandez. Like, Comment, and Share: Analyzing Public Sentiments of Government Policies in Social Media. Philippine Institute for Development Studies, 2023. http://dx.doi.org/10.62986/dp2023.33.

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Social media has become an increasingly important tool for gauging public sentiment, offering real-time insights that can guide policy decisions. This study focuses on analyzing sentiments expressed on the Philippine Institute for Development Studies (PIDS) Facebook page, providing a window into public opinion on various development issues and governmental policies. By conducting opinion mining and sentiment analysis on comments from the top three viral Facebook posts of PIDS, which discuss education, the middle class, and social protection policies, the study reveals a range of public perspec
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Quimba, Francis Mark, and Mark Anthony Barral. Analyzing Filipinos' Openness to Trade Partnerships and Globalization Using Sentiment Analysis. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.53.

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Empirical evidence points to globalization being favorable for a nation’s growth and development. For the Philippines, trade openness and foreign portfolio helped increase per capita GDP as investment and productivity improved. With trade openness and globalization, nations share and gain access to knowledge and technology, inputs of lower costs, new markets, and talents, which improve domestic economic processes. Over the years, however, skepticism about globalization emerged, which affects governments’ foreign strategies and policies and, in turn, the realization of intended benefits. With r
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Zinilli, Antonio. Text Mining in Action: Tools and Techniques using Python. Instats Inc., 2024. http://dx.doi.org/10.61700/k4powzm518m5z1739.

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This seminar provides a comprehensive exploration of text mining techniques using Python, tailored for academic researchers seeking to analyze large textual datasets effectively. Participants will gain hands-on experience with Python libraries and methodologies for natural language processing, sentiment analysis, topic modeling, text classification, and more, enhancing their data analysis capabilities across various disciplines.
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4

Subramanian, Balakrishnan. Opinion mining for breast cancer disease using a priori and K-modes clustering algorithm. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2304p5680995.

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5

Warin, Thierry. The World Health Organization in a Post-COVID-19 Era: An Exploration of Public Engagement on Twitter. CIRANO, 2022. http://dx.doi.org/10.54932/ehuh4224.

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This article analyses the conversations on Twitter related to the World Health Organization (WHO). We collect the text of the discussions as well as the metadata associated with each tweet. Our dataset is exhaustive as it includes all the tweets produced by WHO. Likes, retweets, and replies capture the level of engagement. The goal is to quantify the balance of likes, retweets, and replies, also known as “ratios”, and study their dynamics as proxy for the collective engagement in response to WHO’s communications. Our results demonstrate a higher engagement of the public receiving the informati
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6

Савосько, Василь Миколайович, and Максим Олександрович Квітко. Perspectives and Using Woody Artificial Plantations for Harmonization of the Natural Environment in Kryvyi Rih. Book of Abstracts of the 5th International Scientific Conference. Nitra, 2021. http://dx.doi.org/10.31812/123456789/5440.

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The research aimed to study artificial woody plantations as a significant factor in improving the ecological environment for their further use, adhering to the paradigm of sustainable development in the Kryvyi Rih mining and metallurgical region. In our opinion, the biogeochemical parameters of each seasonal fallen tree leaves can be considered one of the promising markers that determine the viability, or in other words, the health of tree species, and predict the development of artificial woody plantations.
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7

Савосько, Василь Миколайович, and Максим Олександрович Квітко. Perspectives and Using Woody Artificial Plantations for Harmonization of the Natural Environment in Kryvyi Rih. Book of Abstracts of the 5th International Scientific Conference. Nitra, 2021. http://dx.doi.org/10.31812/123456789/5440.

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Abstract:
The research aimed to study artificial woody plantations as a significant factor in improving the ecological environment for their further use, adhering to the paradigm of sustainable development in the Kryvyi Rih mining and metallurgical region. In our opinion, the biogeochemical parameters of each seasonal fallen tree leaves can be considered one of the promising markers that determine the viability, or in other words, the health of tree species, and predict the development of artificial woody plantations.
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8

Савосько, Василь Миколайович, and Максим Олександрович Квітко. Perspectives and Using Woody Artificial Plantations for Harmonization of the Natural Environment in Kryvyi Rih. Book of Abstracts of the 5th International Scientific Conference. Nitra, 2021. http://dx.doi.org/10.31812/123456789/5440.

Full text
Abstract:
The research aimed to study artificial woody plantations as a significant factor in improving the ecological environment for their further use, adhering to the paradigm of sustainable development in the Kryvyi Rih mining and metallurgical region. In our opinion, the biogeochemical parameters of each seasonal fallen tree leaves can be considered one of the promising markers that determine the viability, or in other words, the health of tree species, and predict the development of artificial woody plantations.
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9

Ivaldi, Giles, and Emilia Zankina. Conclusion for the report on the impact of the Russia–Ukraine War on right-wing populism in Europe. European Center for Populism Studies (ECPS), 2023. http://dx.doi.org/10.55271/rp0035.

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This report illustrates the populist performance of the Ukrainian crisis and how Radical Right populists across Europe may have seized the opportunity of the war to instrumentalize war-related economic anxieties and propagate anti-elite and anti-establishment rhetoric. Emphasizing domestic socioeconomic issues did not preclude populist Radical Right parties from using the war as an opportunity to reinforce nationalist sentiment and national pride. Many parties drew parallels between the heroism and sacrifice of the Ukrainian people in defending their nation and nationalist attitudes and devoti
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10

Vizmanos, Jana Flor, Jose Ramon Albert, Mika Muñoz, et al. Addressing Data Gaps with Innovative Data Sources. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.55.

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With the advent of digital transformation, information and communications technology innovations have also led to a "data revolution" wherein more data is being captured, produced, stored, accessed, analyzed, archived, and reanalyzed at an exponential pace. An examination of new data sources, including big data and crowd-sourced data, can complement traditional sources of statistics and unlock insights that can ultimately lead to interventions for better outcomes by informing policies and actions toward attaining robust, sustainable, and inclusive development. This study will examine PIDS webs
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