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Vahedipoor M, Shamsi M, Rasouli Kenari A. Improving Persian Opinion Mining based on Polarity and Balancing Positive and Negative Keywords (case study: Digikala reviews for mobile). JSDP 2024; 20 (4) : 5
URL: http://jsdp.rcisp.ac.ir/article-1-1202-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1202-en.html
Abstract: (635 Views)
In recent years, the massive growth of generated content by the users in social networks and online marketing sites, allows people to share their feelings and opinions in a variety of opinions about different products and services. Sentiment analysis is an important factor for better decision-making that is done using natural language processing (NLP), computational methods, and text analysis to extract the polarity of unstructured documents. The complexity of human languages and sentiment analysis have created a challenging research context in computer science and computational linguistics. Many researchers used supervised machine learning algorithms such as Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Logistic Regression (LR) Random Forest (RF), and deep learning algorithms such as Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM). Some researchers have used Dictionary-based methods. Despite the existence of effective techniques in text mining, there are still unresolved challenges. Note that user comments are unstructured texts; Therefore, in order to structure the textual inputs, parsing is usually done along with adding some features, linguistic interpretations and removing additional items, and inserting the next terms in the database, then extracting the patterns in the structured data and finally the outputs will evaluate and interpret. The imbalance of data with the difference in the number of samples in each class of a dataset is an important challenge in the learning phase. This phenomenon breaks the performance of the classifications because the machine does not learn the features of the unpopulated classes well. In this paper, words are weighted based on the prescribed dictionary to influence the most important words on the result of the opinion mining by giving higher weight. On the other hand, the combination of the adjacent words using n-gram methods will improve the outcome. The dictionaries are highly related to the domain of the application. Some words in an application are important but in mobile comments are not impressive. Another challenge is the unbalanced train data, in which the number of positive sentences is not equal to the number of negative sentences. In this paper, two ideas are applied to build an efficient opinion mining algorithm. First, we build a precise dictionary for mobile Persian comments, and the second idea is to balance the positive and negative comments in train data. In summary, the main achievements of the current research can be mentioned: creating a weighted comprehensive dictionary in the field of mobile phone opinions to increase the accuracy of opinion analysis, balancing positive and negative opinions to improve the accuracy of opinion analysis, and eliminating the negative effect of overfitting and providing a precise approach to Determining the polarity of users' opinions about mobile phones using machine learning and recurrent deep learning algorithms. This new method is presented on mobile phone products from the Digikala site and Senti-Pers data. The result is performed with Naive Bayesian, Support Vector Machine, Stochastic Gradient Descent, Logistic Regression, Random Forest, and deep learning methods such as Convolutional Neural Network and Long Short-Term Memory based on parameters such as Accuracy, Precision, Retrieval, and F-Measure. The proposed method increases accuracy on Digikala, with NB between 10% and 34% and SVM between 5% and 24%, SGD between 7% and 38%, LR between 5% to 38%, and RF between 4% Up to 22% and CNN by 4%. The results show an accuracy increment on Senti-Pers, with NB between 12% and 46% and SVM between 5% and 46%, SGD between 5% and 35%, LR between 6% to 46%, and RF between 4% Up to 46%.
Article number: 5
Type of Study: Research |
Subject:
Paper
Received: 2020/12/31 | Accepted: 2023/12/11 | Published: 2024/04/25 | ePublished: 2024/04/25
Received: 2020/12/31 | Accepted: 2023/12/11 | Published: 2024/04/25 | ePublished: 2024/04/25
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