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Social networks form an important platform for information sharing and interaction among users. The content from social networks can be used to generate recommendations for users in order to help them to choose what they desire. There exist a lot of recommendation methods currently. In this paper, we propose a keyword
social media. Discovering keyword-based correlated networks of these large graphs is an important primitive in data analysis, from which users can pay more attention about their concerned information in the large graph. In this paper, we propose and define the problem of keyword-based correlated network computation over a
propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF
Social media keeps growing and providing us with rich sources of information to understand our everyday lives, customs, and culture in the form of periodic topics. This paper proposes a method of detecting periodic topics based on autocorrelation using the time series of the document frequencies of keywords. To deal
part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85
their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as
Bad news travels fast. Although this concept may be intuitively accepted, there has been little evidence to confirm that the propagation of bad news differs from that of good news. In this paper, we examine the effect of user perspective on his or her sharing of a controversial news story. Social media not only offers insight into human behavior but has also developed as a source of news. In this...
discovery and identification ofemergency situation areas. The system tackles this challengeby providing real-time mapping of influence areas based onautomatic analysis of the flow of discussion using languagedistributions. The workflow is then further enhanced throughthe addition of keyword surprise mapping which projects the
the number of tweets and followers as well as the number of accounts followed and liked.Using Twitter search network API, tweets with “flu” keyword were collected and tabulated. Network centralities were calculated with network analysis tool, NodeXL. The collected Twitters accounts were content analyzed and categorized
information overload. Analyzing social audience who are interested in a company of social media is very difficult and so many text mining methods e.g. fuzzy keyword match method, Twitter LDA method and Machine learning approaches are used for solving this problem. Using the tweets of the account owner to segment followers and
sentimental orientation determination algorithm based on varied TF-IDF. For neologism discovery, statistical data include frequency, duration of appearance and the number of users using a neologism. For sentimental orientation determination, we consider keyword term frequency, and document frequency together and use a varied TF
media topics associated with each topic's keyword analysis. In our research we further explored the potential usage and application of social media analytics tools within local government and found that social media analytics can be of great value for the government in both special events and routine activities. Major
As Internet usage and e-commerce grow, online social media serve as popular outlets for consumers to express sentiments about products. On Amazon, users can tag an album with a keyword, while tweets on Twitter represent a more natural conversation. The differing natures of these media make them difficult to compare
detect user sentiments. The keyword-based approaches for identifying such themes fail to give satisfactory level of accuracy. Here, we address the above problems using statistical text-mining of blog entries. The crux of the analysis lies in mining quantitative information from textual entries. Once the relevant blog
analysis platform to detect the global topics. Our framework targets various countries' social media and extracts keywords from messages written by different languages. Then the framework translates keywords from a local language to English, so that we can understand meanings of keywords. Since our framework is based on
Early adopters of social media have focused on marketing opportunities and are trying to create a presence in key social environments, but this isn't enough. This strategy is much like the initial stages of the Web, when companies thought it was sufficient to simply have a website. Building a Web presence requires a lot of work-building a social media presence requires even more work. Social networking...
and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To
news and social media, extract events related to food hazards, and then organize the data in a structured format for easy consumption. We define an information template for food hazard event based on data from the Korean Ministry of Food and Drug Safety (MFDS), and use the template to aggregate informative keywords from
system aims to detect and extract food hazard event from the live data shared on the Web. We defined information template for food hazard event. Then we used the template to extract informative keywords from the website of Ministry of Food and Drug Safety, the governmental organization responsible for ensuring food safety
described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter, Facebook and other social media, and propose an algorithm to monitor tweets and to detect a target event. We devised a filter of data based on features such as the keywords, the number of times they are present, and
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