Advances in Social Media Analysis by Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, PDF

By Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker

ISBN-10: 3319184571

ISBN-13: 9783319184579

ISBN-10: 331918458X

ISBN-13: 9783319184586

This quantity provides a set of conscientiously chosen contributions within the zone of social media research.
Each bankruptcy opens up a couple of examine instructions that experience the capability to be taken on additional during this speedily starting to be quarter of analysis.
The chapters are different sufficient to serve a few instructions of analysis with Sentiment research because the dominant subject within the book.
The authors have supplied a wide variety of study achievements from multimodal sentiment identity to emotion detection in a chinese language microblogging website.
The e-book could be worthy to analyze scholars, teachers and practitioners within the quarter of social media research.

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Additional info for Advances in Social Media Analysis

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4 Experiments Here, we show the results of our experiments with several variations of the BNgram approach. We focus on two questions. First, what is best slot size to balance topic recall and refresh rate? A very small slot size might lead to missed stories as too few tweets would be analysed; conversely, a very large slot size means that topics would only be discovered some time after they have happened. This low ‘refresh rate’ would reduce the timeliness of the results. Second, what is the best combination of clustering and topic ranking techniques?

The widely used SentiWordNet [6] and SenticNet [9] are two widely known domain-independent lexicons. Supervised learning approaches use machine learning techniques to establish a model from an available corpus of reviews with associated labels. For instance in [5, 10], researchers use the Naive Bayes algorithm to separate positive reviews from negative ones by learning the conditional probability distributions of the considered features in the two classes. , that are later used in a learning algorithm.

In this approach, different features of the text may be extracted from word polarities [7], such as average word polarity, or the number of subjective words, but the distinguishing aspect is that there is no supervised learning. Furthermore, the text is often treated as a bag-of-words; in other words, features are obtained from constituent words without keeping track of the location of those words. Alternatives to the bag-of-word approach are also possible, where word polarities of the first sentence etc.

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Advances in Social Media Analysis by Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker

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