Relevant bibliographies by topics / Sentiment Analysis of Marathi Language

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

Academic literature on the topic 'Sentiment Analysis of Marathi Language'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Sentiment Analysis of Marathi Language"

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Gaikwad, Ramnath Mahadeo, Rajashri Ganesh Kanke, and Manasi Ram Baheti. "Review on Sentiment Analysis of Marathi Language of Maharashtra." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 345–49. http://dx.doi.org/10.22214/ijraset.2023.55149.

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Abstract: Sentiment analysis on numerous Regional languages is performed, and classification algorithms based on Lexicon, Dictionary, and Machine Learning are employed. Because of the widespread usage of social media platforms, people are rapidly turning to the internet to find and discuss information, thoughts, opinions, feelings, perspectives, facts, and suggestions, resulting in a plethora of user-generated emotion enormous amounts of text data available for analysis. A large number of individuals in India express themselves in multiple languages, resulting in a massive amount of Natural La
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Kulkarni, Pallavi V., and Kalpana S. Thakre. "Developing sentiment lexicon for Marathi : A comprehensive survey and analysis." Journal of Information and Optimization Sciences 45, no. 4 (2024): 1141–52. http://dx.doi.org/10.47974/jios-1698.

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Sentiment Analysis plays an important role in developing AI applications involving human language and sense. Language-specific Sentiment Lexicon is important to accelerate Sentiment Analysis. Marathi is a morphologically rich but low-resource Indic Language. The language has a very strong grammatical base showing high resemblance to the human body. The paper elaborates on issues like Lexicon basics, word embedding, and Neural Network Models in the process of Lexicon Construction. Existing databases that are useful as seed databases are enlisted. A detailed study of various methods of Lexicon C
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Nemade, Vedant. "Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29963.

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Sentiment Analysis, pivotal in natural language processing, extends its reach beyond English to Indian regional languages like Hindi, Marathi, Kannada, Konkani, Bengali, Khandeshi, and Urdu. This paper presents a comprehensive survey of 32 research papers in this domain, examining methodologies, datasets, and techniques while emphasizing the significance of sentiment analysis in diverse linguistic contexts for enhancing customer relationship management functionalities. It underscores the necessity for future research and highlights the efficacy of machine learning techniques. By elucidating on
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Sutar, Rishikesh Janardan, and Kamalakar Ravindra Desai. "Sentiment Analysis of Transliterated Hindi and Marathi Using Lexicon-Enriched Transformer Models." International Journal of Environmental Sciences 11, no. 7s (2025): 1228–38. https://doi.org/10.64252/atdz1d85.

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This research introduces a structured approach for sentiment analysis in transliterated Hindi and Marathi, two low-resource Indian languages, through a combination of lexicon-driven data generation and enhanced transformer-based modeling. We began by manually curating sentiment lexicons from two authoritative bilingual dictionaries as Oxford Hindi-English and SalaamChaus Marathi-English, selecting 13,231 Hindi and 9,712 Marathi sentiment-bearing words. Each word was manually annotated with a sentiment weight. To address spelling variability in transliterated text, extensive variant forms were
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Auti, Dr Nisha, Atharva Pujari, Anagha Desai, Shreya Patil, Sanika Kshirsagar, and Rutika Rindhe. "Advanced Audio Signal Processing for Speaker Recognition and Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1717–24. http://dx.doi.org/10.22214/ijraset.2023.51825.

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Abstract: Automatic Speech Recognition (ASR) technology has revolutionized human-computer interaction by allowing users to communicate with computer interfaces using their voice in a natural way. Speaker recognition is a biometric recognition method that identifies individuals based on their unique speech signal, with potential applications in security, communication, and personalization. Sentiment analysis is a statistical method that analyzes unique acoustic properties of the speaker's voice to identify emotions or sentiments in speech. This allows for automated speech recognition systems to
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P. Goje, Swapnil, and Rupali H. Patil. "EXPLORING WORD EMBEDDINGS FOR SENTIMENT ANALYSIS OF MARATHI POLITICAL TWEETS: A MACHINE LEARNING APPROACH." ICTACT Journal on Soft Computing 15, no. 3 (2025): 3608–17. https://doi.org/10.21917/ijsc.2025.0501.

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Sentiment analysis of textual data is becoming increasingly significant in research. Many researchers are developing new technologies to enhance the accuracy and performance of sentiment analysis. This process is particularly vital in analysing customer reviews across various domains. One of new domain which was explored by the researchers is Political domain. After the inception of Smartphones and Internet availability, various political parties are using the social media to influence the people. As every people has their own opinion related to political context, they always try to put it on
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Kakde, Kirti, and H. M. Padalikar. "Context-based Sentiment analysis of Indian Marathi Text using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (2022): 71–76. http://dx.doi.org/10.17762/ijritcc.v10i11.5782.

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In Digital India, the Internet plays a crucial role in communication. The English language is widely used for such a process. The Internet has no language barrier. India is a multi-lingual country with boundless linguistic and social diversities. The most trending pattern observed in India is people intend to post their views, thoughts, feedback, and comments in their mother tongue over social media and blogs. Views posted by people is important for organization belonging to any category small, medium and large enterprises to improve their product or service. This data is hastily accumulated e
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Anita Mahajan. "Empowering Marathi and Hindi Through LLMS: An Implementation of AI Applications in Translation, NLP, and STEM Localization." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 664–74. https://doi.org/10.52783/jisem.v10i36s.6545.

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The paper discusses the paradigm shift of enabling Marathi and Hindi with Large Language Models (LLMs) and focuses extensively on AI-based approaches to localization, translation, NLP, and STEM localization. The paper discusses the transformations of LLMs (GPT-3, BERT, IndicBERT) in local languages' online presence, particularly in India. AI and machine-learning techniques have fueled emphasis on Marathi-to-Hindi translations, and the refinement of STEM localization endeavors, like summarization. A host of challenges exist, including dialect differences and language intricacies, however, we se
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Shah, Sonali Rajesh, Abhishek Kaushik, Shubham Sharma, and Janice Shah. "Opinion-Mining on Marglish and Devanagari Comments of YouTube Cookery Channels Using Parametric and Non-Parametric Learning Models." Big Data and Cognitive Computing 4, no. 1 (2020): 3. http://dx.doi.org/10.3390/bdcc4010003.

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YouTube is a boon, and through it people can educate, entertain, and express themselves about various topics. YouTube India currently has millions of active users. As there are millions of active users it can be understood that the data present on the YouTube will be large. With India being a very diverse country, many people are multilingual. People express their opinions in a code-mix form. Code-mix form is the mixing of two or more languages. It has become a necessity to perform Sentiment Analysis on the code-mix languages as there is not much research on Indian code-mix language data. In t
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Papala, Gowtham, Aniket Ransing, and Pooja Jain. "Sentiment Analysis and Speaker Diarization in Hindi and Marathi Using using Finetuned Whisper." Scalable Computing: Practice and Experience 24, no. 4 (2023): 835–46. http://dx.doi.org/10.12694/scpe.v24i4.2248.

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Automatic Speech Recognition (ASR) is a crucial technology that enables machines to automatically recognize human voices based on audio signals. In recent years, there has been a rigorous growth in the development of ASR models with the emergence of new techniques and algorithms. One such model is the Whisper ASR model developed by OpenAI, which is based on a Transformer encoder-decoder architecture and can handle multiple tasks such as language identification, transcription, and translation. However, there are still limitations to the Whisper ASR model, such as speaker diarization, summarizat
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Dissertations / Theses on the topic "Sentiment Analysis of Marathi Language"

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Erogul, Umut. "Sentiment Analysis In Turkish." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610616/index.pdf.

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Sentiment analysis is the automatic classification of a text, trying to determine the attitude of the writer with respect to a specific topic. The attitude may be either their judgment or evaluation, their feelings or the intended emotional communication. The recent increase in the use of review sites and blogs, has made a great amount of subjective data available. Nowadays, it is nearly impossible to manually process all the relevant data available, and as a consequence, the importance given to the automatic classification of unformatted data, has increased. Up to date, all of the research
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Filho, Pedro Paulo Balage. "Aspect extraction in sentiment analysis for portuguese language." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05122017-140435/.

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Aspect-based sentiment analysis is the field of study which extracts and interpret the sentiment, usually classified as positive or negative, towards some target or aspect in an opinionated text. This doctoral dissertation details an empirical study of techniques and methods for aspect extraction in aspect-based sentiment analysis with the focus on Portuguese. Three different approaches were explored: frequency-based, relation-based and machine learning. In each one, this work shows a comparative study between a Portuguese and an English corpora and the differences found in applying the approa
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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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Alotaibi, Saud Saleh. "Sentiment analysis in the Arabic language using machine learning." Thesis, Colorado State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3720340.

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<p> Sentiment analysis has recently become one of the growing areas of research related to natural language processing and machine learning. Much opinion and sentiment about specific topics are available online, which allows several parties such as customers, companies and even governments, to explore these opinions. The first task is to classify the text in terms of whether or not it expresses opinion or factual information. Polarity classification is the second task, which distinguishes between polarities (positive, negative or neutral) that sentences may carry. The analysis of natural langu
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Nilsson, Ludvig, and Olle Djerf. "How to improve Swedish sentiment polarityclassification using context analysis." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446382.

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This thesis considers sentiment polarity analysis in Swedish. De-spite being the most widely spoken of the Nordic languages less re-search in sentiment has been conducted in this area compared toneighboring languages. As such this is a largely exploratory projectusing techniques that have shown positive results for other languages.We perform a comparison of techniques applied to a CNN to existingSwedish and multilingual variations of the state of the art BERTmodel. We find that the preprocessing techniques do in fact bene-fit our CNN model, but still do not match the results of fine-tuned BERT
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Li, Wenhui. "Sentiment analysis: Quantitative evaluation of subjective opinions using natural language processing." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/28000.

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Sentiment Analysis consists of recognizing sentiment orientation towards specific subjects within natural language texts. Most research in this area focuses on classifying documents as positive or negative. The purpose of this thesis is to quantitatively evaluate subjective opinions of customer reviews using a five star rating system, which is widely used on on-line review web sites, and to try to make the predicted score as accurate as possible. Firstly, this thesis presents two methods for rating reviews: classifying reviews by supervised learning methods as multi-class classification does,
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Dettori, Emilio. "Sentiment Analysis per la moderazione di una community." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Questa tesi, dopo aver illustrato i concetti di Machine Learning e Natural Language Processing, descrive il processo di Sentiment Analysis e le sue applicazioni. L'obiettivo del progetto di tesi è stato quello di studiare un sistema di moderazione automatica testuale di una community online, svolto utilizzando le tre tecniche descritte. In particolare, il fine del progetto è quello di effettuare un'analisi lessicale del testo su un corpus creato appositamente, per poi sviluppare algoritmi di Machine Learning in grado di apprendere da esso. Per ogni tecnologia analizzata sono mostrati esempi e
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Olof, Löfving. "Sentiment Analysis of Equity Analyst Research Reports using Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388586.

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Natural language processing, a subfield of artificial intelligence and computer science, has recently been of great research interest due to the vast amount of information created on the internet in the modern era. One of the main natural language processing areas concerns sentiment analysis. This is a field that studies the polarity of human natural language and generally tries to categorize it as either positive, negative or neutral. In this thesis, sentiment analysis has been applied to research reports written by equity analysts. The objective has been to investigate if there exist a disti
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Westin, Emil. "Fine-grained sentiment analysis of product reviews in Swedish." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424266.

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In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by appr
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Ling, Li, and Simon Larsén. "Sentiment Analysis on Stack Overflow with Respect to Document Type and Programming Language." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229785.

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The sentiment expressed in software engineering (SE) texts has been shown to affect both the productivity and the quality of collaborative work. This is one reason for why sentiment analysis on SE texts has gained attention in research in recent yerars. A large and open resource of SE texts is Stack Overflow (SO). SO is the largest question and answer (Q&amp;A) web site in the Stack Exchange network, and has been the subject for several sentiment analysis studies. It has lately been established that sentiment analyzers trained on social media perform poorly on SE texts, which could challenge t
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Books on the topic "Sentiment Analysis of Marathi Language"

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Franckel, Jean-Jacques. Les figures du sujet: À propos des verbes de perception, sentiment, connaissance. Ophrys, 1990.

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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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Liu, Dilin, and Lei Lei. Conducting Sentiment Analysis. University of Cambridge ESOL Examinations, 2021.

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Liu, Dilin, and Lei Lei. Conducting Sentiment Analysis. University of Cambridge ESOL Examinations, 2021.

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Liu, Bing, Federico Alberto Pozzi, Elisabetta Fersini, and Enza Messina. Sentiment Analysis in Social Networks. Elsevier Science & Technology Books, 2016.

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Das, Dipankar, Erik Cambria, Sivaji Bandyopadhyay, and Antonio Feraco. A Practical Guide to Sentiment Analysis. Springer, 2017.

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Das, Dipankar, Erik Cambria, Sivaji Bandyopadhyay, and Antonio Feraco. A Practical Guide to Sentiment Analysis. Springer, 2018.

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Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications. Elsevier Science & Technology Books, 2024.

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Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications. Elsevier Science & Technology, 2024.

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Hussain, Amir, Erik Cambria, and Ranjan Satapathy. Sentiment Analysis in the Bio-Medical Domain: Techniques, Tools, and Applications. Springer, 2019.

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Book chapters on the topic "Sentiment Analysis of Marathi Language"

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Patil, Rupali S., and Satish R. Kolhe. "Resource Creation for Sentiment Analysis of Under-Resourced Language: Marathi." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0507-9_37.

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Jakate, Mrunmayee, Snehal Lavangare, Nirmiti Bhoir, Aarushi Das, and Deepali Kadam. "A Study on Sentiment Analysis of Twitter Data in Marathi Language for Measuring Depression." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6581-4_22.

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Zhang, Huaping, and Jianyun Shang. "Sentiment Analysis." In Natural Language Processing and Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9739-4_10.

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Shelke, Mahesh B., Saleh Nagi Alsubari, D. S. Panchal, and Sachin N. Deshmukh. "Lexical Resource Creation and Evaluation: Sentiment Analysis in Marathi." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9967-2_19.

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Mane, Deepak, Sarthak Pithe, Hrishikesh Potnis, Soham Nimale, and Madhur Vaidya. "Sentiment Analysis of Marathi Texts Using Deep Learning Models." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-5703-9_57.

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Gezici, Gizem, and Berrin Yanıkoğlu. "Sentiment Analysis in Turkish." In Turkish Natural Language Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7_12.

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Siddiqui, Sanjeera, Azza Abdel Monem, and Khaled Shaalan. "Sentiment Analysis in Arabic." In Natural Language Processing and Information Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41754-7_41.

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Fahad Khan, Zoya, and S. D. Sawarkar. "Sentiment Analysis of Marathi–English Code-Mixed Using Ensemble Model." In Data-Intensive Research. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9179-2_32.

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Miłkowski, Piotr, Marcin Gruza, Przemysław Kazienko, Joanna Szołomicka, Stanisław Woźniak, and Jan Kocoń. "MultiEmo: Language-Agnostic Sentiment Analysis." In Computational Science – ICCS 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08754-7_10.

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Tang, Duyu, and Meishan Zhang. "Deep Learning in Sentiment Analysis." In Deep Learning in Natural Language Processing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5209-5_8.

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Conference papers on the topic "Sentiment Analysis of Marathi Language"

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Gaikwad, Ramnath, Samadhan Nagare, Manasi Baheti, Ahteshamuddin Syed, Santosh Maher, and Pratibha Dapke. "A Machine Learning and DL Approach to Marathi Sentiment Analysis Using Senticnet." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649140.

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Nusrat, Maria, and Muhammad Asfandeyar. "Identify Biasness In Sentiment Analysis." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692520.

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Patil, K. P. Suprith, Anu D, Kishan K, Bhavyashree G, and Abhishek Venkatesh. "Sentiment Analysis in Indian Regional Language (Kannada)." In 2024 Third International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS). IEEE, 2024. https://doi.org/10.1109/aicecs63354.2024.10957647.

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Amburle, Ankita, Vijay Shelake, Ashok Kanthe, and Aditi Malkar. "Aspect Based Call Analysis of Marathi Regional Language in India Using Machine Learning Approach." In 2024 International Conference on Innovation and Novelty in Engineering and Technology (INNOVA). IEEE, 2024. https://doi.org/10.1109/innova63080.2024.10847022.

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Murthy, Anantha, Pallavi Sagar Deshpande, Shweta Gakhreja, Yogita Satish Garwal, Neetu Jain, and Rajeswari Katare. "Natural Language Processing for Sentiment Analysis in Marketing." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877630.

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Ma, Yuchen, Buchao Zhan, Jianhua Yu, and Shankai Yan. "SACMR: Sentiment Analysis in Chinese Language using Modified RoBERTa." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719112.

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Babu, Putta Krishna, J. R. Shruti, Himanth Raj C K, Aishwarya Dandekar, and Shiya Singh. "Sentiment Analysis of Movie Reviews in Regional Language: Kannada." In 2024 5th International Conference on Circuits, Control, Communication and Computing (I4C). IEEE, 2024. http://dx.doi.org/10.1109/i4c62240.2024.10748497.

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Agarwal, Krishna Kant, Shrikant Tiwari, and Vanshika Jain. "Comparative Evaluation of Large Language Models for Sentiment Analysis." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11064177.

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Zhu, Kang, Xuefei Liu, Heng Xie, et al. "Transferring Personality Knowledge to Multimodal Sentiment Analysis." In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800671.

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Patil, Monali Kishor, Nandini Chaudhari, B. V. Pawar, and Ram Bhavsar. "Exploring various emotion-shades for Marathi Sentiment Analysis." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544961.

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Reports on the topic "Sentiment Analysis of Marathi Language"

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Liang, Xiao. Analyzing the Amazon Shopping Experience: A Sentiment Analysis Based on Natural Language Processing (NLP) and Model Comparison. Iowa State University, 2024. http://dx.doi.org/10.31274/cc-20240624-215.

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Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Banco de España, 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses f
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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the cor
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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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