Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

Rajalaxmi Prabhu B., S. S.
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引用次数: 1

Abstract

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.
情感分析的深度学习方法挑战和未来问题
如今,从大型平台、博客、网站和其他评论网站上可以获得大量用户生成的数据。这些数据通常是非结构化的。从这些数据中自动分析情绪被认为是一个重要的挑战。实现了几种机器学习算法来检查来自大型数据集的意见。在理解机器学习分析情感的方法方面已经进行了大量的研究。机器学习主要依赖于模型构建所需的数据,因此也需要采用合适的特征提取技术。在本章中,将讨论几种深度学习方法、挑战和未来的问题。深度学习技术在预测用户情绪方面被认为很重要。本章旨在分析用于预测情绪的深度学习技术,并了解挖掘意见和确定情绪极性的几种方法的重要性。
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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