تحلیل احساسات و موضوع‌کاوی توییت‌های شرکت‌های هواپیمایی ایران (مبتنی بر یادگیری ماشین و مدل‌های زبانی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت ، دانشکده علوم اجتماعی و اقتصاد، دانشگاه الزهرا ، تهران، ایران

2 دانشیار، گروه مدیریت ، دانشکده علوم اجتماعی و اقتصاد، دانشگاه الزهرا ، تهران، ایران

3 دانشجوی دکتری، مدیریت فناوری اطلاعات، دانشکده علوم اجتماعی و اقتصاد، دانشگاه الزهرا ، تهران، ایران

چکیده
با گسترش اینترنت و ظهور رسانه‌های اجتماعی، شرکت‌های هواپیمایی از این بسترها برای تعامل با مشتریان بهره می‌برند. توییتر، به‌عنوان یکی از پرکاربرترین پلتفرم‌های میکروبلاگ، نقشی مهم در ارتباط میان این شرکت‌ها و مسافران دارد. این پژوهش با هدف تحلیل احساسات و موضوعات پیرامون شرکت‌های هواپیمایی ایرانی در توییتر، بر اساس ۲۸۷۶ توییت انگلیسی (آوریل ۲۰۰۸ تا مارس ۲۰۲۴) گردآوری‌شده با Playwright انجام شد. پس از پیش‌پردازش و تفسیر ۲۳۰ ایموجی با هوش مصنوعی، برچسب‌گذاری احساسی با دو رویکرد واژگانی و یادگیری ماشینی (مدل RoBERTa) صورت گرفت. در میان پنج الگوریتم، رگرسیون لجستیک با روش واژگانی و امتیاز F برابر 0.81 بهترین عملکرد را داشت. افزودن اطلاعات ایموجی‌ها دقت مدل‌ها را به‌طور معناداری بهبود بخشید. تحلیل موضوعی با LDA نشان داد «امنیت» اصلی‌ترین دغدغه کاربران است. این نتایج می‌تواند به بهینه‌سازی راهبردهای ارتباطی و خدماتی شرکت‌های هواپیمایی ایرانی کمک کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Sentiment Analysis and Topic Modeling of Tweets about Iranian Airlines: Employing Machine Learning and Language Models

نویسندگان English

Ghazaleh Soltani 1
ameneh khadivar 2
neda paravian 3
1 Department of management, Faculty of Social Sciences and Economics Alzahra University, Tehran, Iran
2 Alzahra University
3 Department of management, Faculty of Social Sciences and Economics Alzahra University, Tehran, Iran
چکیده English

With the expansion of the internet and the emergence of social media, airlines are increasingly using these platforms to engage with customers. Twitter, as one of the most popular microblogging platforms, plays a key role in communication between airlines and passengers. This study aims to analyze sentiments and topics related to Iranian airlines on Twitter, based on 2,876 English tweets collected between April 2008 and March 2024 using Playwright. After preprocessing and interpreting 230 emojis with artificial intelligence, sentiment labeling was performed using both lexicon-based and machine learning approaches (RoBERTa model). Among the five evaluated algorithms, logistic regression with the lexicon-based method achieved the best performance, with an F-score of 0/81. Incorporating emoji information significantly improved model accuracy. Topic modeling with LDA revealed that “safety” was the primary concern of users. These findings can contribute to optimizing the communication and service strategies of Iranian airlines.

کلیدواژه‌ها English

Sentiment analysis
topic modeling
Twitter
deep learning
lexicon-based approach
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