Exploring various emotion-shades for Marathi Sentiment Analysis

Patil, Monali Kishor; Chaudhari, Nandini; Pawar, B. V.; Bhavsar, R. P.

doi:10.1109/asiancon51346.2021.9544961

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…They concluded that the Bag-of-words (BOW) method is the most often utilized way to model text in statistical machine learning approaches to sentiment analysis. [8] Studied various sentiment classification techniques to perform a comparative study of Marathi sentiment analysis. They emphasized the opportunities and challenges researchers encountered regarding Marathi SA and developed a shadedbased approach that falls under the problem of sentiment categorization using a semantic corpus.…”

Section: Language-wise Research On Sentiment Analysismentioning

confidence: 99%

A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Kale,

Prasad,

Potdar

et al. 2023

IJRITCC

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Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy.

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Section: Language-wise Research On Sentiment Analysismentioning

confidence: 99%

A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Kale,

Prasad,

Potdar

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

Classification of Depression on social media using Distant Supervision

Vayadande¹,

Bodhankar²,

Mahajan³

et al. 2022

ITM Web Conf.

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Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues have been spiking all over the world rapidly. People have taken up to social media as a platform to vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets in these times, but the downside being that majority of the population has neglected the importance of mental health issues and there are not enough resources to liberate people about it. Also, people hesitate to talk about their mental issues and seek help. So, a machine learning model using distant supervision to detect depression on Twitter is curated. Use of Sentiment140 dataset with 1.6 million records of different tweets. Our training data makes use of Twitter tweets included with emojis, which are classified as noisy labels on a dataset. Further, this paper mentions about how to use models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost to distinguishing tweets between depressive or nondepressive. The purpose behind using multiple models is to achieve highest accuracy when trained with emoticon dataset. The paper’s main contribution is the idea of using tweets with emoticons for distant supervised learning.

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Recognizing Sentiments In Regional Language Text From Social Media Using Machine Learning Approach: A comparative Study to understand the state of the art tools and associated functionalities available

Sirsat,

Zulpe

2023

2023 International Conference on Advanced Computing Technologies and Applications (ICACTA)

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Exploring various emotion-shades for Marathi Sentiment Analysis

Cited by 3 publications

References 5 publications

A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Classification of Depression on social media using Distant Supervision

Recognizing Sentiments In Regional Language Text From Social Media Using Machine Learning Approach: A comparative Study to understand the state of the art tools and associated functionalities available

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