In an e-commerce environment where millions of transactions take place between providers and users, it is necessary to establish the validity of the service provided. A customer feedback system was provided by market operators to meet this need. But the comments generated are not always reliable. Reviews can positively or negatively affect sales, instead of showcasing the true authenticity of the product or service, from the customer's perspective. Our work proposes an improvement to the traditional feedback system by introducing a trusted reputation system (TRS) that allows filtering valid customers using a set of algorithms, thus creating a degree of trust for the user . Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an Original Essay Consumers in the online marketplace face the problem of filtering the best products from a list of varied options. There are different market operators who offer feedback system to help the customer identify quality products, by looking at the customer's opinion and choosing the product accordingly. Most consumers purchase products based on product reviews. This negatively or positively affects the sale of products. Additionally, it opens the way for spammers to decrease the sales of the product. To eliminate this, the paper focuses on improving the feedback system by introducing the concept of reliability. This can be done through the trusted reputation system. TRS are programs that allow users to evaluate each other. Using such methods can help reduce the number of spammers, potentially increasing the number of genuine reviews. The good thing about these reviews is that they help determine the authenticity of the product. Sentiment analysis has been studied in many areas of the field, such as movie reviews, educational reviews, product reviews, e-learning, hotel reviews, and many more. Most researchers have focused on analyzing quantitative data. However, some studies have been done on qualitative data using sentiment analysis. We found six works mentioning the idea of ​​using opinion mining and sentiment analysis in education. Algorithms such as Naive Bayes, k-means and support vector machines are used in opinion classification. . The article also focuses on the real reputation system. There are several architectures of reputation truth systems having different algorithms to calculate the reputation score linked to the product. Many authors have proposed in their work several TRS architectures with different algorithms to calculate the reputation score linked to the product. Also, some academic work on the Truthreputation system has been devoted to the inclusion of the semantic analysis of feedback in the calculation of the product's trust score and especially the user's degree of trust. Even in studies attempting to provide more complex reputation methods, some issues are still not considered, such as the credibility of arbiters, updating the user's degree of trust in any intervention, the age of the rating and feedback or even the concordance between the given note which is a scalar value and the textual feedback associated with it. Unlike the mentioned TRS, the proposed design overcomes these problems and uses an algorithm that includes analysis of textual comments in order to calculate the degree ofuser trust giving feedback and a trust reputation score for the product. the online market the problem of filtering the best reviews or comments for purchasing products. We try to eliminate the problem by listing the best reviews so that it becomes easy for customers to choose a product by analyzing the experiences of other consumers, allowing them to post their reviews. Consumers who use the online marketplace may sometimes purchase substandard products. Although the e-commerce company offers facilities like return and exchange of products, the process sometimes becomes a tedious task. The project aims to provide customers with the opportunity to select the desired products based on the rating of the item they want or are considering purchasing, which has been evaluated based on the rating and reviews provided by consumers using a TruthReputation. System (TRS). The Opinion Mining of our project will be based on sentiment analysis algorithms and methods as well as the Truth Reputation System algorithm. Trusted Reputation Systems (TRS) will provide the information needed to help parties make the right decision in an electronic transaction. In fact, as a security provider in electronic services, TRS must faithfully calculate the most reliable score for a targeted product or service. Thus, TRS must rely on a robust architecture and suitable algorithms capable of selecting, storing, generating and classifying notes and feedback. In the proposed architecture, for each user wishing to leave a rating (rating) and feedback (semantic review), we analyze the customer's attitude towards a number of short and selected feedbacks and by-products stored in the knowledge base. This user's opinion will be accessible to any other user. Next, we assume that we have a path relaying all users (the nodes). Accordingly, we need to know the user's trust level and determine the trust level of the comments. Trusted Reputation System Design Algorithm Description The customer starts by giving a rating and a textual comment on a specific product. When he clicks on submit, in order to validate the information provided, we will redirect the user to another interface displaying this message for example: “please give us your opinion on the following feedback before validating the information you have given below: » this interface we will find selected returns from the database of different types. These feedbacks can be fabricated to summarize the numerous user feedbacks stored in the database. The feedback generated can be stored in another knowledge base. So, as much as we will add feedbacks into the regular database, we will also populate the knowledge base with pre-made feedbacks using algorithms and text mining tools. However, some users may provide already summarized feedback that can be directly included in the knowledge base. Indeed, there are many text mining and data mining algorithms and tools that could search for the most appropriate feedback that is primarily product-related and can summarize and summarize most of each user type? feedbacks.In fact, before sending customer comments and rating about the product to the trusted reputation system, we need to check the concordance between them in order to avoid and eliminate contradictions or malware attacking our system. In the redirected interface, we will display severalreturns of different types. However, the user can specify how many comments they want to like or dislike. Of course, we can also specify the minimum and maximum number of feedbacks to be displayed by the user. In fact, through this redirection we try to detect and analyze the user's intention behind their intervention on the commerce application. Therefore, we examine and evaluate its intention using other prefabricated feedback of different types. Of course, we already have the reliability of every return. Therefore, we use our reputation algorithm studied in the section to generate the user's degree of confidence which plays the role of coefficient then rectifies his assessment according to his degree of confidence and generates the feedback score. In fact, each refers reliability to a threshold. The closer the reliability is to 5, the more reliable the comments. The closer the reliability is to -5, the more unreliable the feedback. If the comments are trustworthy, their score would be included in otherwise it would be included in the B.TRS algorithm. The reputation algorithm used in this TRS uses the analysis of semantic comments in order to generate a trust reputation score for the product. In fact, we have 3 types of reviews: Positive feedback: represents opinions that express a positive point of view about the product. These improving reviews contain positive content about the product. Second, the adjective positive refers to the nature of the content of the feedback, not its reliability. However, every feedback, regardless of its type, can have either positive or negative reliability. Whether positive or negative, it is progressive: it has degrees like a float within a threshold of . Negative Feedback: represents opinions that speak negatively about the product. Logically, users giving such reviews are not satisfied with the reviewed product. These comments may be true, contrary to the truth, or far from the truth. This is why the reliability of each feedback is represented by a floating number between -5 and 5. Attenuated feedback: represents feedback that speaks positively about certain aspects of the product and negatively about other aspects. They are also characterized by the reliability included in the contradiction feedbacks: represent feedbacks with contradictory content, for example, a feedback where the user does not talk about the specified product but about another or he claims that the camera of a cell phone is great and later in the same review he says the camera is very bad. In fact, we must start by detecting the return of contradictions. We then need an algorithm and semantic analysis tool that can detect contradiction in specific product-related content. We can customize the analysis depending on the product. For example, if the user says that "the swimming pool of the hotel that cannot afford it is not clean", the algorithm must be able to detect this great contradiction. We can give the algorithm for each input product the property of the algorithm; if there is no similarity, we can consider it a contradiction. But the agreement of course includes the meaning. Because if the customer writes that the negative point of this hotel is that there is no swimming pool. He is telling the truth so obviously the presence of an absent property in feedback does not mean that there is a contradiction. In fact, before sending customer comments and appreciation of the product to the trusted reputation system, we must check the concordance and alliance between them so as not to have contradiction. After checking the..