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measure sentiment using a binary choice keyword algorithm and a multi-knowledge based approach is proposed using, Self-Organizing Maps and tourism domain knowledge in order to model sentiment. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model to maximums and minimums in the
Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social
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
present the experiment design to capture and extract the viewing patterns in Twitter using the eye-tracking technology. We show a set of experiment results based on the analysis of eye gazing data, in order to demonstrate how the subjects look for specified keywords in the Twitter timeline, which can further contribute to
In this paper we mine over 80 million twitter micro logs in order to explore whether data from this social media initiative can be used to identify sentiment about tourism and Thailand amid the unrest in that country during the early part of 2010 and further whether analysis of tweets can be used to discern the effect
As the usage of social networks grows day by day, a single person can reach hundreds or thousands of people in a minute. Micro blogging is the new era of social communication, which can be used anywhere thanks to mobile phones. People spend hours and use social networks extensively, expressing their feelings
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
Twitter is a user-friendly social network which deserves its real-time nature. With the help of an algorithm, the investigation can be made with regard to some of the real-time events such as earthquake. The target event is assumed and classified based on the keywords, number of words and their context. The
. Precise extraction of valuable information from short text messages posted on social media (Twitter) is a collaborative task. In this paper, we analyze tweets to classify data and sentiments from Twitter more precisely. The information from tweets are extracted using keyword based knowledge extraction. Moreover, the
. GeoContext includes methods for filtering a social media stream by keywords and location coordinates in order to provide more specific topics. GeoContext includes a geolocation module, called GeoContext Locator, for predicting the locations of tweets that are not associated with explicit coordinates, in order to model topics in
identifying Tweets that describe cases with acute and more critical symptoms from those referring to milder cases. We found that making use of mereley very small n-gram keyword lexica, the automatic identification of critical cases reaches an accuracy of 92%.
In this paper we tackle the recently proposed problem of hidden streams. In many situations, the data stream that we are interested in, is not directly accessible. Instead, part of the data can be accessed only through applying filters (e.g. keyword filtering). In fact this is the case of the most discussed social
Opinion mining deals with the determination of people's sentiments over the social web as positive, negative or neutral. Microblogs such as twitter serves as a rich source for opinion mining and sentiment analysis as opinions are shared by millions of users. In our paper, we have analyzed the various methods available
Twitter is a public social service that allows users to share information as short-text messages. Previous researchers have tried to analyze the information available on Twitter to discover topic trending. However, these topics are associated with the whole network, and are not associated to a particular place. In
document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a
synchronized with Twitter messages by specific keywords like “rain” or/and “landslide” to analyse the relationship between environmental phenomena and social responses in heavy rain conditions in the Hiroshima region. The results were analysed and visualized through a geovisualization technique to
The main objective of this paper is to compare the sentiments that prevailed before and after the presidential elections, held in both US and France in the year 2012. To achieve this objective we extracted the content information from a social medium such as Twitter and used the tweets from electoral candidates and
In this paper we present a framework that extracts meaningful knowledge from microposts shared in social platforms in order to build user profiles. This process involves different steps for the analysis of such microposts (extraction of keywords, named entities and their matching to ontological concepts) and their
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