Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it ...Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it keeps tracking user feedback. The sheer volume of online reviews makes it difficult for a human to process and extract all significant information to make purchasing choices. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews. In this paper, we present a hybrid algorithm that combines an auto-summarization algorithm with a sentiment analysis (SA) algorithm, to offer a personalized user experiences and to solve the semantic-pragmatic gap. The algorithm consists of six steps that start with the original text document and generate a summary of that text by choosing the N most relevant sentences in the text. The tagged texts are then processed and then passed to a Naive Bayesian classifier along with their tags as training data. The raw data used in this paper belong to the tagged corpus positive and negative processed movie reviews introduced in [1]. The measures that are used to gauge the performance of the SA and classification algorithm for all test cases consist of accuracy, recall, and precision. We describe in details both the aspect of extraction and sentiment detection modules of our system.展开更多
Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or mis...Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading re- marks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang （SPG） detection. First, we analyze user sentiment orientation to topics based on emo- tional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network （SON） where each vertex represents a user account on a so- cial media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User ac- counts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.展开更多
文摘Online reviews are considered of an important indicator for users to decide on the activity they wish to do, whether it is watching a movie, going to a restaurant, or buying a product. It also serves businesses as it keeps tracking user feedback. The sheer volume of online reviews makes it difficult for a human to process and extract all significant information to make purchasing choices. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews. In this paper, we present a hybrid algorithm that combines an auto-summarization algorithm with a sentiment analysis (SA) algorithm, to offer a personalized user experiences and to solve the semantic-pragmatic gap. The algorithm consists of six steps that start with the original text document and generate a summary of that text by choosing the N most relevant sentences in the text. The tagged texts are then processed and then passed to a Naive Bayesian classifier along with their tags as training data. The raw data used in this paper belong to the tagged corpus positive and negative processed movie reviews introduced in [1]. The measures that are used to gauge the performance of the SA and classification algorithm for all test cases consist of accuracy, recall, and precision. We describe in details both the aspect of extraction and sentiment detection modules of our system.
文摘Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading re- marks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang （SPG） detection. First, we analyze user sentiment orientation to topics based on emo- tional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network （SON） where each vertex represents a user account on a so- cial media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User ac- counts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.
