Armina Mohseni; ameneh khadivar; Fatemeh Abbasi
Abstract
The growth of the Internet, social networks and e-commerce websites provide a platform for users to express their opinions. In recent years, many users have expressed their positive or negative opinions about food, service, and quality and restaurant atmosphere online. These comments are very important ...
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The growth of the Internet, social networks and e-commerce websites provide a platform for users to express their opinions. In recent years, many users have expressed their positive or negative opinions about food, service, and quality and restaurant atmosphere online. These comments are very important for the decision of other users as well as restaurants to maintain quality, product development and their brand. Sentiment analysis is a natural language processing approach and allows systematic analysis of users' opinions. Due to the importance of this issue, the purpose of this study is to present a model for analyzing the sentiment of TripAdvisor's comments about Iranian restaurants. In this research, we propose an aspect-based sentiment analysis based on a deep learning algorithm which is the standard long short-term memory neural network to extract users' sentiments about restaurants. To teach the model, 4000 comments were labeled according to four aspects in three classes of not related, positive and negative, and the study steps were done based on Crisp methodology. Accuracy for food, service, value and atmosphere were 82%, 86%, 87% and 81%, respectively. These results indicate the efficiency and acceptable performance of the model for aspect-based sentiment analysis of restaurants. Furthermore, food and atmosphere are the most important aspects for the customers of Iranian restaurants, respectively. Restaurant owners can use the developed model to gain a competitive advantage and find their strengths and weaknesses.
Mohammad Ehsan Basiri; Shirin Habibi; Shahla Nemati
Abstract
With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about ...
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With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about the Covid-19 epidemic in Iran. This study analyzes the emotions in the opinions of the Iranian people on the social network Twitter during the Corona crisis. For this purpose, a deep neural network model is presented. As there is no labeled dataset of Covid-19 tweets, the proposed model is first trained on the Stanford University Sentiment140 dataset, which contains 1.6 million tweets, and then used to classify the two classes of emotions contained in the collected corona-related tweets in Iran. The results show that the percentage of tweets with negative emotions is significantly higher than positive tweets. Also, the change in negative emotions of people in different months is proportional to the change in patient statistics.
Shahriar Mohammadi; Eslam Nazemi
Abstract
Social media data is one of the most effective and accurate indicators of public sentiment, so that analyzing this information can provide researchers with interesting results from users' sentiment about characters, subjects, products, and services. In this study, while reviewing users' opinion on Twitter ...
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Social media data is one of the most effective and accurate indicators of public sentiment, so that analyzing this information can provide researchers with interesting results from users' sentiment about characters, subjects, products, and services. In this study, while reviewing users' opinion on Twitter about the various features of two competing mobile phone products on the market, Apple's Iphone X and Samsung's Galaxy S9, we examine their sentiment based on the gender of consumers of these two products. This study is performed using the relation-based method in the feature extraction step and Lexicon-Based in the polarity of opinions step. The results of this study show that the popularity of different product features varies between male and female users, and based on these results, business owners can produce products that focus on people's gender or design smart advertising plan according to their interests. These measures ultimately lead to increased business profitability and customer satisfaction.
Sara Afgababaei; Mohsen Nazari; Nastaran Haji Heydari
Abstract
in today's competitive world, pricing, as one of the 4 elements of marketing mix, plays a key role in the success or failure of companies. Based on theoretical principles, in value-based pricing, companies calculate the distinct value of their products from customer insights. The purpose of this ...
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in today's competitive world, pricing, as one of the 4 elements of marketing mix, plays a key role in the success or failure of companies. Based on theoretical principles, in value-based pricing, companies calculate the distinct value of their products from customer insights. The purpose of this research, which is based on design science, is to design and implement a system to help competitive pricing based on customer value by categorizing and analyzing customer emotions with the aim of maximizing profits. choice of this research is The hoteling industry , in which customer opinions and values play key rule. In this research, first, it has been tried to identify and classify important and valuable items in the minds of the customers of these 5-star hotels in Tehran by the method of topic modeling with LDA algorithm. Then, in each category, each customer's comments are tagged, and these comments processed with different text mining algorithms. Then the accuracy of the algorithms calculated. the deep learning algorithm with 0.9 accuracy had the highest accuracy of calculations. Finally, the analysis of sentiments with the available data was performed by the system designed in the previous step and acceptable results were obtained. The application of the proposed system is to identify the value of hotels in the minds of target customers over competitors and to help value-based pricing.
Fatemeh Abbasi; Ameneh Khadivar; Mohsen Yazdinejad
Abstract
Nowadays, people use others' opinions on social networks for decision-making to purchase online products and services. Likewise, the companies which offer the products employ sentiment analysis of opinions of users and customers to adopt informed decisions and offer new products. Considering the ...
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Nowadays, people use others' opinions on social networks for decision-making to purchase online products and services. Likewise, the companies which offer the products employ sentiment analysis of opinions of users and customers to adopt informed decisions and offer new products. Considering the high volume of the contextual data, conversion, and analysis of such data is a major challenge in e-commerce. Sentiment analysis is a modern approach in the extraction of opinions. The obtained information from sentiment analysis can have a considerable impact on the efficient selection of customers. In the present study, a model has been proposed for sentiment analysis of users' opinions for buying a cell phone in Digikala. This study is applicable to the objective aspect. The data includes users' opinions in Digikala. The statistical sample consists of opinions of cell phone users in Digikala. Supervised learning, as well as Python package, were utilized for analysis and implementation. A model has been proposed for sentiment analysis of users' opinions. The results demonstrate that this model can classify users' opinions with an accuracy equal to 0.892. Similarly, the results reveal that users' opinions about ease of use, possibilities, and capabilities of the cell phone are positive and about purchase value to price, innovation, design and appearance, and quality of cell phones are negative. The proposed model can be implemented in e-commerce websites like Digikala and its output can be observed by users systematically. Finally, it can be led to inform decision-making for buyers and companies which offer products.
Fatemeh Abbasi; Babak Sohrabi; Amir Manian; Ameneh Khadivar
Abstract
In recent years, the growth of social networks and, consequently, the increasing content of these networks have led people to use others’ opinions to make decisions for the purchase and use of products, services or even political choices. Given the fact that users' comments are textual and ...
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In recent years, the growth of social networks and, consequently, the increasing content of these networks have led people to use others’ opinions to make decisions for the purchase and use of products, services or even political choices. Given the fact that users' comments are textual and their reading and summarizing is timely and difficult, the automation of the extraction of opinions and sentiments of users' comments is one of the suggested solutions for online sales sites to provide more efficient services to customers for better decision making. Sentiment analysis or opinion mining is a process where people's opinions, feelings and attitudes are extracted in relation to a particular subject and are recognized as a branch of the text mining. The results of sentiment analysis can be used in recommender systems to provide more effective shopping suggestions. Information derived from the opinion mining can be used in a variety of fields such as libraries for better choices and purchases based on the users' real opinions. In this research, a system for automatically categorizing the sentiments expressed in the opinions of the buyers of the Amazon book website is presented. The system is designed using ensemble voting models to analyze the sentiment of Amazon users' comments. For all analyses, Python text mining packages are used. In ensemble method two methods are used: majority voting and weight-based voting. In the weighting method, a greater weight is assigned to a classifier by higher accuracy. By comparing the performance of the results, the weighting model is chosen as the final model for making the sentiment analysis. Results show that the proposed system can automatically classify positive and negative comments with an accuracy of over 80%.
