乌尔都语基于方面的情感分析:资源创建与评估

Amna Altaf, Muhammad Waqas Anwar, Muhammad Hasan Jamal, Usama Ijaz Bajwa, Sadaf Rani
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引用次数: 0

摘要

随着网络互动的发展和在线社交网络使用量的增加,情感分析越来越受欢迎。体育、健康、音乐和技术等话题在在线社交网络,尤其是在 Twitter 上被广泛讨论。人们用自己的母语分享他们的活动、观点和对不同事件的感受,通过情感分析可以了解人们对这些事件的情感。对于英语语言,情感分析的研究成果非常丰富。然而,对于乌尔都语这种资源稀缺的语言,情感分析方面的研究却少之又少。在这项研究中,我们使用机器学习和深度学习分类器从句子中提取以下信息,即方面术语、方面术语极性、方面类别和方面类别极性,从而对乌尔都语的体育推文进行基于方面的情感分析。这是首次使用经典机器学习和深度学习方法对乌尔都语进行基于方面的情感分析。此外,我们还识别了句子中的隐含方面。我们提出的方法表明,经典机器学习方法在方面术语极性、方面类别和方面类别极性任务中表现更佳,而深度学习模型在方面术语任务中的表现优于经典机器学习分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Aspect-based sentiment analysis in Urdu language: resource creation and evaluation

Aspect-based sentiment analysis in Urdu language: resource creation and evaluation

With the advancement in web interactions and increased use of Online Social Networks, sentiment analysis has gained popularity. Topics like sports, health, music, and technology are widely debated on in OSN, especially on twitter. People share their activities, views, and feelings toward different events in their native languages that can be analyzed using sentiment analysis to understand the sentiments of the people toward these events. For English language, studies on sentiment analysis are vastly available. However, very little work exists on sentiment analysis for resource-scarce language like Urdu. For this study, we perform aspect-based sentiment analysis on sports tweets in Urdu language by extracting the following information from a sentence, i.e., aspect terms, aspect term polarity, aspect category, and aspect category polarity, using machine learning and deep learning classifiers. This work is the first effort in aspect-based sentiment analysis for Urdu language using classical machine learning and deep learning approach. Additionally, we also identify implicit aspects from a sentence. Our proposed approach shows classical machine learning approach performed better on the tasks of aspect term polarity, aspect category, and aspect category polarity, while deep learning model outperformed classical machine learning classifiers for the task of aspect term/s.

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