Vidyashree K P, Rajendra A B
Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
SN Comput Sci. 2023;4(2):190. doi: 10.1007/s42979-022-01607-x. Epub 2023 Feb 2.
Sentiment analysis is one of the effective techniques for mining the opinion from shapeless data contains text like review of the products, review of the movie. Sentiment analysis is used as a key to gather response from consumers, reviews of brands, marketing analyses, and political campaigns. In the subject of natural processing, performing sentiment analysis using the data obtained from Twitter is considered as a new study in these days. The dataset is gathered using the Twitter API and the Twitter package. The analysis of Twitter data is a process which takes place automatically by text data analysis to determine the view of public on the specified topic. Here, an improvised sentimental analysis model is proposed to identify the polarity of the tweets such as positive, neutral and negative. In this paper, stochastic gradient descent (SGD) algorithm uses stochastic gradient neural network (SGNN) to categorize the sentiment analysis on basis of tweets provided by the Twitter users and the proposed stochastic gradient descent optimization Algorithm based on stochastic gradient neural network (SGDOA-SGNN) provides better performance when compared with the existing Forest-Whale Optimization Algorithm based on deep neural network F-WOA-DNN model.
情感分析是从包含产品评论、电影评论等文本的无形状数据中挖掘观点的有效技术之一。情感分析被用作收集消费者反馈、品牌评论、市场分析和政治活动的关键。在自然处理领域，使用从推特获得的数据进行情感分析在当今被视为一项新研究。数据集是使用推特应用程序编程接口（Twitter API）和推特包收集的。推特数据分析是一个通过文本数据分析自动进行的过程，以确定公众对指定主题的看法。在此，提出了一种改进的情感分析模型，以识别推文的极性，如积极、中性和消极。本文中，随机梯度下降（SGD）算法使用随机梯度神经网络（SGNN）根据推特用户提供的推文对情感分析进行分类，与现有的基于深度神经网络的森林鲸鱼优化算法（F-WOA-DNN模型）相比，所提出的基于随机梯度神经网络的随机梯度下降优化算法（SGDOA-SGNN）具有更好的性能。
