مدل‌سازی موضوعی و تحلیل احساس‌های مشتریان با استفاده از روش‌های پردازش زبان طبیعی و یادگیری ماشین

نوع مقاله : پژوهشی

نویسندگان

1 دانشکده مهندسی نساجی، دانشگاه صنعتی امیرکبیر، تهران، ایران

2 دانشکده‌ی مهندسی صنایع و سیستم‌های مدیریت، دانشگاه صنعتی امیرکبیر، تهران، ایران.

چکیده

در نوشتارحاضر، به بررسی و تجزیه­و­تحلیل نظرهای مشتریان پوشاک زنان با استفاده از روش­های یادگیری ماشین و پردازش زبان طبیعی پرداخته­ شده است. مدل­های یادگیری ماشین استفاده‌شده، شامل: ماشین بردار پشتیبان، رگرسیون لجستیک، درخت تصمیم، جنگل تصادفی، بیز ساده‌ی چندجمله­ای، بیز ساده‌ی مکمل، XGBoost ، و LightGBM بوده‌اند. به‌منظور برداری‌کردن و استخراج ویژگی متن نظرها از الگوریتم‌های TF-IDF و Word2vec استفاده شده است. مدل­سازی موضوعی با استفاده از روش تخصیص دیریکله‌ی پنهان و خوشه­بندی K-Means انجام شده است. مجموعه‌ی داده‌ی استفاده‌شده مربوط به نظرهای پوشاک زنان و متغیر هدف، امتیازهای مشتریان در نظرها بوده است. مطالعه‌ی حاضر، در حالت‌های: دو­کلاسه، سه­کلاسه، و پنج­کلاسه انجام و در هر سه حالت، بهترین عملکرد مربوط به مدل جنگل تصادفی با دقت 98/0 در حالت دوکلاسه، 95/0 در حالت سه­کلاسه، و 91/0 در حالت پنج­کلاسه برآورد شده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Topic Modeling and Sentiment Analysis of Customers Using Natural Language Processing and Machine Learning Techniques

نویسندگان [English]

  • Razieh Rahimi 1
  • Reza Ghasemy Yaghin 2
1 1Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran.
2 Department of Industrial Engineering and Management Systems, Amirkabir University of Technology.
چکیده [English]

Given that modeling and predicting customer behavior using data science helps companies gain a better understanding of customer behavior, this research focuses on analyzing customer reviews in the women’s clothing domain within e-commerce. We employ machine learning techniques and natural language processing (NLP) to achieve this goal. The machine learning models used include Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Multinomial Naive Bayes, Complement Naive Bayes, XGBoost, and LightGBM. To extract and vectorize text features from the reviews, we utilize the TF-IDF and Word2vec algorithms. We employ Topic Modeling using the Latent Dirichlet Allocation (LDA) method and k-means clustering. The dataset consists of women’s clothing reviews, with the target variable being customer ratings in those reviews. The study is conducted in binary, three-class, and five-class scenarios. The target variable, which originally has five classes (scores 1 to 5), is categorized into two-class and three-class modes. In the two-class mode, scores below 3 are class zero, while scores of 3 and above are class one. In the three-class mode, scores below 3 are class zero, scores equal to 3 are class one, and scores above 3 are class two. In all three cases, the Random Forest model performs best, achieving an accuracy of 0.98 in the binary case, 0.95 in the three-class case, and 0.91 in the five-class case. After performing the required preprocessing and feature engineering, principal component analysis (PCA) and T SNE are applied. After that, the scatter diagram of the data is drawn and the optimal number of clusters, 3, is estimated using the elbow and silhouette diagrams. In the next step, by removing punctuation marks, stop words and words with fewer than three letters, converting the first letter of the words to lowercase and lemmatization, data cleaning was done. After that, topic modeling was done and each of the topics and words related to them were examined. In the next step, the topics were examined in different clusters.   These analyses provide a comprehensive understanding of the key themes and concerns customers have when considering womenswear items in each of the four topics.

کلیدواژه‌ها [English]

  • Sentiment analysis
  • machine learning
  • natural language processing
  • text mining
  • customer behavior

مراجع

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