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

被引：0
|
作者：
Kavitha, K. M. [1 ]
Nishmitha, A. [1 ]
Balgopal, Gowda Karthik [1 ]
Naik, Kausalya K. [1 ]
Gaonkar, Mranali Gourish [1 ]
机构：
[1] Visvesvaraya Technol Univ, St Joseph Engn Coll Mangaluru, Dept Comp Sci & Engn, Belagavi, Karnataka, India
来源：
2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA | 2022年
关键词：
sentiment analysis; VADER; polarity lexica; opinionated texts;
D O I：
10.1109/ICMLA55696.2022.00235
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：1498 / 1503
页数：6
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