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Sentiment Analysis for Twitter : Going Beyond Tweet Text
Analysing sentiment of tweets is important as it helps to determine the ...
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A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
Social media platforms like twitter and facebook have be- come two of th...
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Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum
We examine the relationship between social structure and sentiment throu...
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A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media
Text sentiment analysis for preliminary depression status estimation of ...
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Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness
Various domain users are increasingly leveraging real-time social media ...
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Sensing Social Media Signals for Cryptocurrency News
The ability to track and monitor relevant and important news in real-tim...
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A Classifiers Voting Model for Exit Prediction of Privately Held Companies
Predicting the exit (e.g. bankrupt, acquisition, etc.) of privately held...
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Anxious Depression Prediction in Real-time Social Data
Mental well-being and social media have been closely related domains of study. In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed. This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector <word, timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is analyzed for irregularities and opinion polarity analytics is done to find inconsistencies in posting behaviour. The model is trained using three classifiers (multinomial naïve bayes, gradient boosting, and random forest) and majority voting using an ensemble voting classifier is done. Preliminary results are evaluated for tweets of sampled 100 users and the proposed model achieves a classification accuracy of 85.09
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