By Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker
This quantity provides a suite of rigorously chosen contributions within the quarter of social media research.
Each bankruptcy opens up a couple of examine instructions that experience the capability to be taken on extra during this speedily transforming into region of study.
The chapters are varied adequate to serve a few instructions of analysis with Sentiment research because the dominant subject within the book.
The authors have supplied a vast diversity of study achievements from multimodal sentiment id to emotion detection in a chinese language microblogging website.
The booklet should be important to analyze scholars, teachers and practitioners within the sector of social media research.
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Extra info for Advances in Social Media Analysis
In: Proceedings of the ACM 2011 conference on Computer Supported Co-operative Work, pp. 355–358. ACM (2011) 40. : A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28(1), 11–21 (1972) 41. : Live blogging—digital journalism’s pivotal platform? uk. Digital Journalism 1(1), 82–101 (2013) 42. : Automatic extraction of soccer game events from Twitter. In: Proceedings of the Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web (2012) 43.
2]. 1 The three data sets differ in the rates of tweets, determined by the popularity of the topic and the choice of filter keywords. The mean tweets per minute (tpm) were: Super Tuesday, 832 tpm; FA Cup, 1293 tpm; and US elections, 2209 tpm. eu/results/datasets/72-twitter-tdt-dataset. 30 C. Martin et al. 41 s respectively. This means that a user interface displaying these topics could be updated every 1–2 min to show the current top-10 (or top-m) stories. To generate the sets of Tweets used in the evaluation, we crawled Twitter during the events using appropriate sets of filter keywords, such as the names of the participants.
We show that even changes in the polarity of a small number of words affect the overall accuracy by a few percent. Next, we propose a sentence-based analysis of the review sentiment, using the updated polarity lexicon for feature extraction. While word-level polarities provide a simple, yet effective method for estimating a review’s polarity, the gap from word-level polarities to review polarity is too big. The use of sentence-based analysis is aimed to bridge this gap. The remainder of this chapter is organized as follows.