Sentiment analysis model for Airline customers’ feedback using deep learning techniques

Heba Allah Samir, Laila Abd-Elmegid, Mohamed Marie
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Abstract

Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.
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基于深度学习技术的航空公司客户反馈情感分析模型
情感分析(SA)最近开发了一种自动化的方法来评估情绪、情感和这些评论或意见,以从基于文本的数据中提取相关的主观信息。分析Twitter等社交网络上的情绪,已经成为了解用户意见、更好地理解和满意度的有力手段。然而,分散和收集客户的调查需要花费时间和精力,而且往往不准确和不一致,并且评估和提高情感分析方法的准确性受到自然语言处理(NLP)中遇到的挑战的阻碍。本文使用自然语言处理、文本分析、生物识别和计算语言学从Skytrax航空公司客户反馈的SACF数据中检测和提取回复、情绪或情绪。本研究使用深度学习模型来分析应用于小型SACF的各种方法来解决情感分析问题。我们应用词嵌入(手套嵌入模型)来提高一系列广泛用于特征提取的数据集的情感分类性能。最后,利用深度学习(DL)对SACF数据分析进行了比较研究,以评估不同模型和输入特征的性能,这些模型和输入特征是循环神经网络(RNN)、长短期记忆(LSTM)、门控循环单元(GRU)、1D卷积神经网络(CONV1D)和双向编码器表示从变压器(BERT)应用于2019年的大数据集。使用每种分类技术对该方法进行评估;确定了情感分析的精度、召回率、f1分数和准确性指标。结果表明,LSTM在分类精度上优于LSTM;结果为91%。
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来源期刊
CiteScore
7.50
自引率
6.10%
发文量
17
审稿时长
15 weeks
期刊介绍: The International Journal of Engineering Business Management (IJEBM) is an international, peer-reviewed, open access scientific journal that aims to promote an integrated and multidisciplinary approach to engineering, business and management. The journal focuses on issues related to the design, development and implementation of new methodologies and technologies that contribute to strategic and operational improvements of organizations within the contemporary global business environment. IJEBM encourages a systematic and holistic view in order to ensure an integrated and economically, socially and environmentally friendly approach to management of new technologies in business. It aims to be a world-class research platform for academics, managers, and professionals to publish scholarly research in the global arena. All submitted articles considered suitable for the International Journal of Engineering Business Management are subjected to rigorous peer review to ensure the highest levels of quality. The review process is carried out as quickly as possible to minimize any delays in the online publication of articles. Topics of interest include, but are not limited to: -Competitive product design and innovation -Operations and manufacturing strategy -Knowledge management and knowledge innovation -Information and decision support systems -Radio Frequency Identification -Wireless Sensor Networks -Industrial engineering for business improvement -Logistics engineering and transportation -Modeling and simulation of industrial and business systems -Quality management and Six Sigma -Automation of industrial processes and systems -Manufacturing performance and productivity measurement -Supply Chain Management and the virtual enterprise network -Environmental, legal and social aspects -Technology Capital and Financial Modelling -Engineering Economics and Investment Theory -Behavioural, Social and Political factors in Engineering
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