Scholarlink

<p>Sentiment Analysis (SA) has achieved significant growth in recent decades among the evolving applications, including social networks and decision-support systems. Recently, the proliferation of data volumes for Dravidian languages has made SA a significant task to retrieve useful information. The prevailing works are focused more on the monolingual analysis and lack a complete study of the recent trends. The research presents a survey on the evolving landscape of SA in Indian languages with a specific emphasis on Dravidian languages. An overview of SA, emphasizing the levels and the purpose of the analysis is presented. Moreover, the generic process is explained with the major advancements of pre-trained architectures applied in recent works. Along with the various classification methodologies, this study analyzes the strengths conforming to the various training databases and languages, mainly focused on sentiment classification. Furthermore, an exhaustive view of the existing challenges and the applications is provided, offering unique linguistic guidance in the field of SA.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An exhaustive review of the recent advancements in sentiment analysis on Dravidian languages

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

查看全文
  • Namitha Shambhu Bhat,
  • Kuldeep Sambrekar,
  • Venkatesh Bhandage,
  • Prashant Y. Niranjan

摘要

Sentiment Analysis (SA) has achieved significant growth in recent decades among the evolving applications, including social networks and decision-support systems. Recently, the proliferation of data volumes for Dravidian languages has made SA a significant task to retrieve useful information. The prevailing works are focused more on the monolingual analysis and lack a complete study of the recent trends. The research presents a survey on the evolving landscape of SA in Indian languages with a specific emphasis on Dravidian languages. An overview of SA, emphasizing the levels and the purpose of the analysis is presented. Moreover, the generic process is explained with the major advancements of pre-trained architectures applied in recent works. Along with the various classification methodologies, this study analyzes the strengths conforming to the various training databases and languages, mainly focused on sentiment classification. Furthermore, an exhaustive view of the existing challenges and the applications is provided, offering unique linguistic guidance in the field of SA.