Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling
نویسندگان
چکیده
منابع مشابه
Sentiment Analysis in Social Networks through Topic modeling
In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) ...
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This paper proposes a method to extract sentiment topics from a text collection. The method utilizes sentiment clues and a relaxed labeling schema to extract sentiment topics. Experiments with a quantitative and a qualitative evaluations was done to confirm the performance of the method. The quantitative evaluation with a polarity classification marked the accuracy of 0.701 in tweets and 0.691 ...
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Classifying the sentiment of documents is a well-studied problem in Natural Language Processing (NLP). The existence of excellent discriminative classifiers like Maxent has pushed the main body of research in the direction of feature engineering. In this paper, I examine an unusual class of features, the document-topic proportions assigned by the Latent Dirichlet Allocation topic model. In part...
متن کاملTopic Modeling and Classification of Cyberspace Papers Using Text Mining
The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspac...
متن کاملImproving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training
In this paper, we present multiple approaches to improve sentiment analysis on Twitter data. We first establish a state-of-the-art baseline with a rich feature set. Then we build a topic-based sentiment mixture model with topic-specific data in a semi-supervised training framework. The topic information is generated through topic modeling based on an efficient implementation of Latent Dirichlet...
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ژورنال
عنوان ژورنال: PLOS ONE
سال: 2019
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0226025