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Twitter is one of the most popular social media networks in the world. It is also mostly used by corporate companies, media as well as individual users. Media organizations use Twitter to announce about the news. Although the language of the given news is formal and preferred words to share information are different for each organization. In this study, we proposed an approach to recognize the Twitter...
The social media generates large volume of data through tweets and text messages during and after any disaster. The analysis and classification of the obtained data at the time of disaster is essential for conveying the information to the appropriate rescue personnel. In this paper, an automated text classification system is proposed in order to classify the data effectively. The classification of...
This paper examines the changes in consumer behaviour and opinions due to the transition from a public to a commercial broadcaster in the context of broadcasting international media events. By analyzing TV viewer ratings, Facebook activity and its sentiment, we aim to provide answers to how the transition from airing Winter Olympic Games on NRK to TV2 in Norway affected consumer behaviour and opinion...
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...
We explore the use of social media data to reduce the effort in developing a conversational speech corpus. The LOTUSSOC corpus is created by recording Twitter messages through a mobile application. In the first phase, which took around one month, 172 hours of speech from 208 speakers were recorded and ready for use without the need for speech segmentation and transcription. In terms of language similarity...
While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer...
Document classification or document categorization is one of the most studied areas in computer science due to its importance. The problem is to assign a document using its text to one or more classes or categories from a predefined set. We propose a new approach for fast text classification using randomized explicit semantic analysis (RS-ESA). It is based on a state of the art approach for word sense...
The increasing amount of information available in electronic media is a strategic resource for the health professional who uses this source to support decision making. However, it is not feasible to analyze a significant number of documents in a short time without the support of a computational tool. The Information Extraction aims to extract from textual documents only the relevant information defined...
For higher text classification precision, a general fusion classification model and algorithm are proposed, which based on model theory of information fusion, adopting multi-Media information on the network. The model includes two layers, one is feature layer, which deals with different Media information with different classification algorithm, and inputs the classification results into the higher...
Much has been documented in the literature on sentiment analysis and document summarisation. Much of this applies to long structured text in the form of documents and blog posts. With a shift in social media towards short commentary (see Facebook status updates and twitter tweets), the difference in comment structure may affect the accuracy of sentiment analysis techniques. From our VoiceYourView...
Style-based text authorship identification extracts features from authorship-known texts, constructs classifier and then identifies disputed texts. Authorship identification belongs to the domain of style classification and is a branch of text classification. In contrast with text classification which deals with the content of texts, authorship identification focuses on the form property of texts...
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