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A Study on Sentiment Analysis Using Various ML/NLP Models on Historical Data of Indian Leaders
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</div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87354</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">A Study on Sentiment Analysis Using Various ML/NLP Models on Historical Data of Indian Leaders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarthak%20Deshpande">Sarthak Deshpande</a>, <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Patil"> Akshay Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Pradip%20Pandhare"> Pradip Pandhare</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Wankhede"> Nikhil Wankhede</a>, <a href="https://publications.waset.org/abstracts/search?q=Rushali%20Deshmukh"> Rushali Deshmukh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Among the highly significant duties for any language most effective is the sentiment analysis, which is also a key area of NLP, that recently made impressive strides. There are several models and datasets available for those tasks in popular and commonly used languages like English, Russian, and Spanish. While sentiment analysis research is performed extensively, however it is lagging behind for the regional languages having few resources such as Hindi, Marathi. Marathi is one of the languages that included in the Indian Constitution鈥檚 8th schedule and is the third most widely spoken language in the country and primarily spoken in the Deccan region, which encompasses Maharashtra and Goa. There isn鈥檛 sufficient study on sentiment analysis methods based on Marathi text due to lack of available resources, information. Therefore, this project proposes the use of different ML/NLP models for the analysis of Marathi data from the comments below YouTube content, tweets or Instagram posts. We aim to achieve a short and precise analysis and summary of the related data using our dataset (Dates, names, root words) and lexicons to locate exact information. <iframe src="https://publications.waset.org/abstracts/181045.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilingual%20sentiment%20analysis" title="multilingual sentiment analysis">multilingual sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Marathi" title=" Marathi"> Marathi</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=lexicon-based%20approaches" title=" lexicon-based approaches"> lexicon-based approaches</a> </p> <a href="https://publications.waset.org/abstracts/181045/a-study-on-sentiment-analysis-using-various-mlnlp-models-on-historical-data-of-indian-leaders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181045.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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