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- All Subjects: Sentiment Analysis
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
This thesis studies the area of sentiment analysis and its general uses, benefits, and limitations. Social networking, blogging, and online forums have turned the Web into a vast repository of comments on many topics. Sentiment analysis is the process of using software to analyze social media to gauge the attitudes or sentiments of the users/authors concerning a particular subject. Sentiment analysis works by processing (data mining) unstructured textual evidence using natural language processing and machine learning to determine a positive, negative, or neutral measurement. When utilized correctly, sentiment analysis has the potential to glean valuable insights into consumers' minds, which in turn leads to increased revenue and improved customer satisfaction for businesses. This paper looks at four industries in which sentiment analysis is being used or being considered: retail/services, politics, healthcare, and finances. The goal of the thesis will be to explore whether sentiment analysis has been used successfully for economic or social benefit and whether it is a practical solution for analyzing consumer opinion.
ContributorsSoumya, Saswati (Author) / Uday, Kulkarni (Thesis director) / Brooks, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / WPC Graduate Programs (Contributor) / Department of Information Systems (Contributor)
Created2014-05
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