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- Genre: Academic theses
- Creators: Baral, Chitta
- Creators: Wang, Yilin
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
Description
Due to vast resources brought by social media services, social data mining has
received increasing attention in recent years. The availability of sheer amounts of
user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information
in social networks could provide another rich source in deriving implicit information
for social data mining. However, the vast majority of existing studies overwhelmingly
focus on positive links between users while negative links are also prevailing in real-
world social networks such as distrust relations in Epinions and foe links in Slashdot.
Though recent studies show that negative links have some added value over positive
links, it is dicult to directly employ them because of its distinct characteristics from
positive interactions. Another challenge is that label information is rather limited
in social media as the labeling process requires human attention and may be very
expensive. Hence, alternative criteria are needed to guide the learning process for
many tasks such as feature selection and sentiment analysis.
To address above-mentioned issues, I study two novel problems for signed social
networks mining, (1) unsupervised feature selection in signed social networks; and
(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In
particular, I model positive and negative links simultaneously for user preference
learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and
implicit sentiment signals from signed social networks into a coherent model Signed-
Senti. Empirical experiments on real-world datasets corroborate the effectiveness of
these two frameworks on the tasks of feature selection and sentiment analysis.
received increasing attention in recent years. The availability of sheer amounts of
user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information
in social networks could provide another rich source in deriving implicit information
for social data mining. However, the vast majority of existing studies overwhelmingly
focus on positive links between users while negative links are also prevailing in real-
world social networks such as distrust relations in Epinions and foe links in Slashdot.
Though recent studies show that negative links have some added value over positive
links, it is dicult to directly employ them because of its distinct characteristics from
positive interactions. Another challenge is that label information is rather limited
in social media as the labeling process requires human attention and may be very
expensive. Hence, alternative criteria are needed to guide the learning process for
many tasks such as feature selection and sentiment analysis.
To address above-mentioned issues, I study two novel problems for signed social
networks mining, (1) unsupervised feature selection in signed social networks; and
(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In
particular, I model positive and negative links simultaneously for user preference
learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and
implicit sentiment signals from signed social networks into a coherent model Signed-
Senti. Empirical experiments on real-world datasets corroborate the effectiveness of
these two frameworks on the tasks of feature selection and sentiment analysis.
ContributorsCheng, Kewei (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2017