In the modern era, Internet usage has become a basic necessity in the lives of people. Nowadays, people can perform online shopping and check the customer’s views about products that purchased online. Social networking services enable users to post opinions on public platforms. Analyzing people’s opinions helps corporations to improve the quality of products and provide better customer service. However, analyzing this content manually is a daunting task. Therefore, we implemented sentiment analysis to make the process automatically. The entire process includes data collection, pre-processing, word embedding, sentiment detection and classification using deep learning techniques. Twitter was chosen as the source of data collection and tweets collected automatically by using Tweepy. In this paper, three deep learning techniques were implemented, which are CNN, Bi-LSTM and CNN-Bi-LSTM. Each of the models trained on three datasets consists of 50K, 100K and 200K tweets. The experimental result revealed that, with the increasing amount of training data size, the performance of the models improved, especially the performance of the Bi-LSTM model. When the model trained on the 200K dataset, it achieved about 3% higher accuracy than the 100K dataset and achieved about 7% higher accuracy than the 50K dataset. Finally, the Bi-LSTM model scored the highest performance in all metrics and achieved an accuracy of 95.35%.

中文翻译：

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具有深度学习模型的快餐公司的情感分析

在现代时代，互联网的使用已成为人们生活中的基本必需品。如今，人们可以进行在线购物，并查看客户对在线购买产品的看法。社交网络服务使用户可以在公共平台上发布意见。分析人们的意见有助于企业提高产品质量并提供更好的客户服务。但是，手动分析此内容是一项艰巨的任务。因此，我们实施了情感分析以使流程自动进行。整个过程包括使用深度学习技术进行数据收集，预处理，单词嵌入，情感检测和分类。Twitter被选为数据收集源，并使用Tweepy自动收集了推文。本文采用了三种深度学习技术，分别是CNN，Bi-LSTM和CNN-Bi-LSTM。在三个数据集上训练的每个模型都由50K，100K和200K推文组成。实验结果表明，随着训练数据量的增加，模型的性能特别是Bi-LSTM模型的性能得到改善。当模型在200K数据集上训练时，其精度比100K数据集高约3％，比50K数据集高约7％。最终，Bi-LSTM模型在所有指标中均获得了最高的性能，并达到了95.35％的准确性。特别是Bi-LSTM模型的性能。当模型在200K数据集上训练时，其精度比100K数据集高约3％，比50K数据集高约7％。最终，Bi-LSTM模型在所有指标中均获得了最高的性能，并达到了95.35％的准确性。特别是Bi-LSTM模型的性能。当模型在200K数据集上训练时，其精度比100K数据集高约3％，比50K数据集高约7％。最终，Bi-LSTM模型在所有指标中均获得了最高的性能，并达到了95.35％的准确性。