基于面向的英语和印地语自以为是的社交媒体文本情感分析

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI:10.1109/ICMLA55696.2022.00235

Kavitha Karimbi Mahesh, A. Nishmitha, Gowda Karthik Balgopal, Kausalya K Naik, Mranali Gourish Gaonkar

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引用次数: 0

摘要

我们提出了一种基于词典的方法来分类英语和印地语中固执己见的社交媒体文本。进一步探讨了连词、程度修饰语、否定、表情符号和表情符号在评价意见表达强度方面的作用。使用人工构建的印地语极性词典，我们对2,717篇印地语评论进行分类，准确率达到86.45%。对YouTube评论的实时分析显示，英语评论分类任务的准确率为86%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Aspect-based Sentiment Analysis of English and Hindi Opinionated Social Media Texts

We present a lexicon-based approach for classifying opinionated social media texts in English and Hindi. The effect of conjunctions, degree modifiers, negations, emojis and emoticons in scoring the intensity of opinion expressed is further explored. Using a manually built Hindi polarity lexicon, we achieve an accuracy of 86.45% in classifying 2,717 Hindi reviews. A real-time analysis on YouTube reviews showed 86% accuracy for English review classification task.

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2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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