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- All Subjects: Sentiment Analysis
- Genre: Academic theses
- Creators: Candan, Kasim S
- Creators: Wang, Yilin
Description
Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various other types of sites in the United States and around the world. Given the fundamentally emotional nature of humans and the amount of emotional content that appears in Web 2.0 content, it is important to understand how such websites can affect the emotions of users. This work attempts to determine whether emotion spreads through an online social network (OSN). To this end, a method is devised that employs a model based on a general threshold diffusion model as a classifier to predict the propagation of emotion between users and their friends in an OSN by way of mood-labeled blog entries. The model generalizes existing information diffusion models in that the state machine representation of a node is generalized from being binary to having n-states in order to support n class labels necessary to model emotional contagion. In the absence of ground truth, the prediction accuracy of the model is benchmarked with a baseline method that predicts the majority label of a user's emotion label distribution. The model significantly outperforms the baseline method in terms of prediction accuracy. The experimental results make a strong case for the existence of emotional contagion in OSNs in spite of possible alternative arguments such confounding influence and homophily, since these alternatives are likely to have negligible effect in a large dataset or simply do not apply to the domain of human emotions. A hybrid manual/automated method to map mood-labeled blog entries to a set of emotion labels is also presented, which enables the application of the model to a large set (approximately 900K) of blog entries from LiveJournal.
ContributorsCole, William David, M.S (Author) / Liu, Huan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Candan, Kasim S (Committee member) / Arizona State University (Publisher)
Created2011
Description
Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity.
One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding.
In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither
visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.
One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding.
In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither
visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Liu, Huan (Committee member) / Tong, Hanghang (Committee member) / Chang, Yi (Committee member) / Arizona State University (Publisher)
Created2018