基于深度学习的图像情感分析

Nimitha N, S. Bala, V. Satheeswaran, M. Janani, S. Selvanayaki, S. Ramasami
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摘要

情感分析是一种强大的工具,近年来在企业中越来越受欢迎。它包括分析文本、语音或图像,以理解它们所传达的情感或观点。这个过程可以对客户偏好、行为和满意度水平提供有价值的见解。通过利用情感分析,企业可以收集客户对其产品和服务的反馈,这可以帮助他们确定需要改进的领域,并建立更牢固的客户关系。此外,情感分析还可以用于支持情感营销活动,使企业能够创建更有针对性和个性化的信息,从而与受众产生共鸣。例如,通过分析社交媒体上的帖子和评论,企业可以更好地了解他们的品牌是如何被感知的,并相应地调整他们的营销策略。一种流行的方法是迁移学习,其中使用预训练的模型来分析新的数据集。深度学习算法,如卷积神经网络(cnn),在包括图像情感分析在内的各种应用中取得了特别成功的准确结果。然而,分析通过图像传达的情感是一项复杂的任务,还有很大的改进空间。Inception-v3技术是该领域的一个显著发展,因为它可以很容易地识别身体的关键部位,比如面部,这对准确检测情绪至关重要。将该模型的结果与其他各种机器学习方法的结果进行比较,建议的模型显示出高达99.5%的更高精度水平。本研究展示了使用深度学习算法和迁移学习方法来改进图像情感分析的潜力。例如,在医疗保健领域,图像情感分析可以用来检测患者的情绪表达,这对于评估他们的精神和情绪状态是有价值的。在销售和市场营销中,它可以用来评估客户对不同产品或活动的情绪反应,从而使企业能够相应地调整自己的策略。总体而言,使用图像情感分析可以为各行各业的企业提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning based Sentiment Analysis on Images
Sentiment analysis is a powerful tool that has been gaining popularity among businesses in recent years. It involves analyzing text, speech, or images to understand the emotions or opinions conveyed by them. This process can provide valuable insights into customer preferences, behavior, and satisfaction levels. By leveraging sentiment analysis, businesses can gather customer feedback on their products and services, which can help them identify areas for improvement and develop stronger customer relationships. Furthermore, sentiment analysis can also be used to support emotional marketing campaigns, allowing businesses to create more targeted and personalized messaging that resonates with their audience. For example, by analyzing social media posts and comments, businesses can gain a better understanding of how their brand is perceived and adjust their marketing strategies accordingly. One popular method for this is transfer learning, where pre-trained models are used to analyze new datasets. Deep learning algorithms, such as convolutional neural networks (CNNs), have been particularly successful in achieving accurate results in a variety of applications, including image sentiment analysis. However, analyzing emotions conveyed through images is a complex task, and there is still much room for improvement. The Inception-v3 technique is a notable development in this field, as it can easily identify key parts of the body, such as the face, which is essential for accurately detecting emotions. The results of this model were compared to those of various other machine learning approaches, and the suggested model showed superior accuracy levels of up to 99.5%. This research demonstrates the potential of using deep learning algorithms and transfer learning methods to improve image sentiment analysis. For example, in the field of Healthcare, image sentiment analysis can be used to detect emotional expressions of patients, which can be valuable for assessing their mental and emotional state. In Sales and Marketing, it can be used to evaluate customers' emotional responses to different products or campaigns, allowing businesses to tailor their strategies accordingly. Overall, the use of image sentiment analysis can provide valuable insights to businesses across a wide range of industries.
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