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Text actionability detection is the problem of classifying user authored natural language text, according to whether it can be acted upon by a responding agent. In this paper, we propose a supervised learning framework for domain-aware, large-scale actionability classification of social media messages. We derive lexicons, perform an in-depth analysis for over 25 text based features, and explore strategies...
Viral marketing, a marketing strategy that leverages the influence power in intimate relationship, has become more prevalent due to the popularity of online social networking services in recent years. Consumers are more likely to make a purchase based on social media referrals. Since marketing through social media and traditional channels may target on different audiences, how to maximize the revenue...
The widespread use of social media and the internet are emerging trends that offer an additional interaction channel for companies to better understand customer sentiments about their brands and products. Sentiment analysis uses text data from social media such as customer comments and reviews, which has the nature of high dimensionality. Without selection, typically there are at least thousands of...
Since the social media hype started in the early 2000s, the Internet has bloomed with user-generated data. The content generated by users in social media varies from blogs, forums, social network platforms, and video sharing communities. This data has a special emphasis on the relationships among users of the community. As a consequence, social media data contains significant information about their...
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