[PDF] Top 20 Summarization of Social Media Data Using Topic Detection
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Summarization of Social Media Data Using Topic Detection
... technique using TF-IDF method on tweet streams to provide useful summary to user with regard to ...of social sites ...topics using Dynamic LM classifier that provides accuracy above 60% and then ... See full document
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Detection of Topic and its Extrinsic Evaluation Through Multi Document Summarization
... We can see from Table 2 that Rouge-1 obtained by our approach was also the best compared to the baselines. Table 2 also shows the performance of other research sites reported by (Celikylmaz and Hakkani-Tur, 2010). The ... See full document
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A Systematic Review on the Suspicious Profiles Detection on Online Social Media Data
... board, Social website data. This data actually signify massive virtual space, where anyone can hold discussion in the form of posted ...any topic is achieved by monitoring the suspicious ... See full document
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A hybrid approach for Sarcasm Detection of Social Media Data
... trained data set assumed to have knowledge of sarcasm, analyser adds it to the knowledge ...tags using combinational approaches involving POS logic [4], emoticons [3] ... See full document
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An Application of Big Data in Social Media Anomaly Detection using Weight Based Technique to Compare Performance of PIG and HIVE
... Classification algorithm: the table 4 contains the profile classification algorithm. In this context the given algorithm accepts profile weights and the profile type . And after computation/ evaluation of profiles the ... See full document
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Burstyn Topic Detection from Twitter Data Using Topicsketch
... news media report on the incident came ...news media, or any other manual effort, to capture most of such bursty topics in real-time even though their reporting crew can pick up a subset of the trending ... See full document
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Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
... Other work on disagreement recognition in- cludes that of (Wang et al., 2011) who de- scribe conditional random field model for detect- ing (dis)agreement between speakers in English broadcast conversations. They use ... See full document
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Topic Aware Neural Keyphrase Generation for Social Media Language
... of social media keyphrase prediction, where extractive ap- proaches are widely employed (Zhang et ...to social media language owing to the data spar- sity ...latent topic ... See full document
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Twitter Trending Topic Summarization Using Speech Act
... of social media such as facebook, whatsup,skype, viber, twitter, linked in,twitter is most common and widely used social ...the data,but it may it may be subject to some ...,Preprocessing, ... See full document
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Detection and Control of Urban Emergency Events using Social Media Big Data
... This paper aims to improve the understanding on how LBSN can be used as a reliable source of spatio-temporal information, by analysing the temporal, spatial and social dynamics of Twitter activity during a major ... See full document
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Prediction of User Topic Opinions based on Social Media Analysis
... Twitter provides an API, which in turn provides access to entire Twitter restful API methods. We can provide one or more and can retrieve the data accordingly. The results from the called API methods will be ... See full document
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Incorporating social role theory into topic models for social media content analysis
... Twitter data shared by Kwak et ...by using the retweeting links of these seed users (including both retweet in and out ...of social interactions, we discard users with very few tweets or very few ... See full document
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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... the social media is one of such kind of application where easily data is obtainable by the ...in social media text mining and ...work social media text analysis based ... See full document
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Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality
... the topic terms and keyword earthquake are neglected due to their occurrence in nearly every tweet; 3) some functional #hashtags and news agencies such as #fb and #BBC are excluded because they are usually ... See full document
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Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling
... facilitates social interactions in online spaces, the rise of anti-social behaviour in online spaces has attracted the attention of ...in social media ...Aggression Detection in ... See full document
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Effective Mining of Social Media Data with Fake News Detection Using Graph Clustering Model
... and the incentive model they offer to popularize news, make them appealing targets for malicious and fraudulent behaviors. Some fraudulent developers deceptively boost search rank and popularity of their news (e.g., ... See full document
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Social Media Topic Categorization Using Hierarchical Clustering Approach
... In data mining and machine learning techniques unsupervised learning is used for analyzing a huge amount of data with no class ...the data available is in large quantity and the pre-known patterns ... See full document
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SOCIAL MEDIA DATA ANALYSIS USING HADOOP
... topic. For example: “I am so happy today, good morning to everyone”, is a general positive text, and the text: “Django is such a good movie, highly recommends 10/10”, expresses positive sentiment toward the movie, ... See full document
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Topic Modeling: Construct Analysis for Public Issues Using Social Media
... summarizing data streams using count-min sketch method, “An improved Data Stream Summary: The Count-Min Sketch and its Applications” this journal is fully constructed with the help of mathematical ... See full document
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Towards Summarization for Social Media Results of the TL;DR Challenge
... found in this corpus are true summaries provided by the authors of a post, they often abstract over a subject matter, and they cover a much wider range of topics than generally found in news articles. Ta- ble 1 shows a ... See full document
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