Network analysis is increasingly important across various fields, including the fragrance industry, where perfumes are represented as nodes and shared user preferences as edges in perfume networks. Community detection can uncover clusters of similar perfumes, providing insights into consumer preferences, enhancing recommendation systems, and informing targeted marketing strategies. This study aims to apply community detection techniques to group perfumes favored by users into relevant clusters for better recommendations. We constructed a bipartite network from user reviews on the Persian retail platform "Atrafshan," with nodes representing users and perfumes, and edges formed by positive comments. This network was transformed into a Perfume Co-Preference Network, connecting perfumes liked by the same users. By applying community detection algorithms, we identified clusters based on shared preferences, enhancing our understanding of user sentiment in the fragrance market. To improve sentiment analysis, we integrated emojis and a user voting system for greater accuracy. Emojis, aligned with their Persian counterparts, captured the emotional tone of reviews, while user ratings for scent, longevity, and sillage refined sentiment classification. Edge weights were adjusted by combining adjacency values with user ratings in a 60:40 ratio, reflecting both connection strength and user preferences. These enhancements led to improved modularity of detected communities, resulting in more accurate perfume groupings. This research pioneers the use of community detection in perfume networks, offering new insights into consumer preferences. Our advancements in sentiment analysis and edge weight refinement provide actionable insights for optimizing product recommendations and marketing strategies in the fragrance industry.
Introduction: Microblogging websites have massed rich data sources for sentiment analysis and opinion mining. In this regard, sentiment classification has frequently proven inefficient because microblog posts typically lack syntactically consistent terms and representatives since users on these social networks do not like to write lengthy statements. Also, there are some limitations to low-resource languages. The Persian language has exceptional characteristics and demands unique annotated data and models for the sentiment analysis task, which are distinctive from text features within the English dialect. Method: This paper first constructs a user opinion dataset called ITRC-Opinion in a collaborative environment and insource way. Our dataset contains 60,000 informal and colloquial Persian texts from social microblogs such as Twitter and Instagram. Second, this study proposes a new architecture based on the convolutional neural network (CNN) model for more effective sentiment analysis of colloquial text in social microblog posts. The constructed datasets are used to evaluate the presented architecture. Furthermore, some models, such as LSTM, CNN-RNN, BiLSTM, and BiGRU with different word embeddings, including Fasttext, Glove, and Word2vec, investigated our dataset and evaluated the results. Results: The results demonstrate the benefit of our dataset and the proposed model (72% accuracy), displaying meaningful improvement in sentiment classification performance.
Aspect-based sentiment analysis is of great importance and application because of its ability to identify all aspects discussed in the text. However, aspect-based sentiment analysis will be most effective when, in addition to identifying all the aspects discussed in the text, it can also identify their polarity. Most previous methods use the pipeline approach, that is, they first identify the aspects and then identify the polarities. Such methods are unsuitable for practical applications since they can lead to model errors. Therefore, in this study, we propose a multi-task learning model based on Convolutional Neural Networks (CNNs), which can simultaneously detect aspect category and detect aspect category polarity. creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews. We evaluated the proposed method using a Persian language dataset in the movie domain. Jacquard index and Hamming loss measures were used to evaluate the performance of the developed models. The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.
Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-tasks of aspect-based sentiment analysis. The goal in ACD is to specify which aspect of the entity comes up in opinion while ACP aims to specify the polarity of each aspect category from the ACD task. The previous works mostly propose separate solutions for these two sub-tasks. This paper focuses on the ACD and ACP sub-tasks to solve both problems simultaneously. The proposed method carries out multi-label classification where four different deep models were employed and comparatively evaluated to examine their performance. A dataset of Persian reviews was collected from CinemaTicket website including 2200 samples from 14 categories. The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics. The results indicate the high applicability and preference of the CNN and GRU models in comparison to LSTM and Bi-LSTM.
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this rich language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of high-level tasks -- Reading Comprehension, Textual Entailment, etc. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5$k$ new instances across 6 distinct NLU tasks. Besides, we present the first results on state-of-the-art monolingual and multi-lingual pre-trained language-models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.
This paper focuses on how to extract opinions over each Persian sentence-level text. Deep learning models provided a new way to boost the quality of the output. However, these architectures need to feed on big annotated data as well as an accurate design. To best of our knowledge, we do not merely suffer from lack of well-annotated Persian sentiment corpus, but also a novel model to classify the Persian opinions in terms of both multiple and binary classification. So in this work, first we propose two novel deep learning architectures comprises of bidirectional LSTM and CNN. They are a part of a deep hierarchy designed precisely and also able to classify sentences in both cases. Second, we suggested three data augmentation techniques for the low-resources Persian sentiment corpus. Our comprehensive experiments on three baselines and two different neural word embedding methods show that our data augmentation methods and intended models successfully address the aims of the research.