Inferring the sentiment of social media content, for instance blog postings or online product reviews, is both of great interest to businesses and technically challenging to accomplish. This paper presents two computational methods for estimating social media sentiment which address the challenges associated with Web-based analysis. Each method formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and assumes that only limited prior information is available regarding the sentiment orientation of any of the documents or words of interest. The first algorithm is a semi-supervised sentiment classifier which combines knowledge of the sentiment labels for a few documents and words with information present in unlabeled data, which is abundant online. The second algorithm assumes existence of a set of labeled documents in a domain related to the domain of interest, and leverages these data to estimate sentiment in the target domain. We demonstrate the utility of the proposed methods by showing they outperform several standard methods for the task of inferring the sentiment of online reviews of movies, electronics products, and kitchen appliances. Additionally, we illustrate the potential of the methods for multilingual business informatics through a case study involving estimation of Indonesian public opinion regarding the July 2009 Jakarta hotel bombings.
Now Let us understand social media analytics. Social media analytics is the practice of gathering data from social media websites or networks such as Facebook, Twitter, Google plus, etc. and analyzing those metrics to understand insights to make business decisions. Most often people confuses between web analytics and Social media Analytics, let us have a clear demarcation between these two. Web analytics uses the data collected directly from a particular business website and Social media analytics uses the data collected from social media networks. In general, Web Analytics tells you about your traffic levels, referral sources, bounce rate and user behaviour on your website.
Wang, Yilin (Arizona State University) | Wang, Suhang (Arizona State University) | Tang, Jiliang (Arizona State University) | Liu, Huan (Arizona State University) | Li, Baoxin (Arizona State University)
Recently text-based sentiment prediction has been extensively studied, while image-centric sentiment analysis receives much less attention. In this paper,we study the problem of understanding human sentiments from large-scale social media images,considering both visual content and contextual information,such as comments on the images, captions,etc. The challenge of this problem lies in the “semantic gap” between low-level visual features and higher-level image sentiments. Moreover, the lack of proper annotations/labels in the majority of social media images presents another challenge.To address these two challenges, we propose a novel Unsupervised SEntiment Analysis (USEA) framework for social media images. Our approach exploits relations among visual content and relevant contextual information to bridge the “semantic gap” in the prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.
Du, Rundong (Georgia Institute of Technology) | Lu, Zhongming (Georgia Institute of Technology) | Pandit, Arka (Georgia Institute of Technology) | Kuang, Da (Georgia Institute of Technology) | Crittenden, John (Georgia Institute of Technology) | Park, Haesun (Georgia Institute of Technology)
We propose to introduce social media opinion mining research into the field of computational sustainability. Opinion mining from social media can be a faster and less expensive alternative to traditional survey and polling, on which many sustainability research are based. We describe a framework for such analysis, examine the challenges in our proposed framework and current status of research on those challenges. We also propose some possible research directions for tackling these challenges.