18,722 research outputs found
Discovering conversational topics and emotions associated with Demonetization tweets in India
Social media platforms contain great wealth of information which provides us
opportunities explore hidden patterns or unknown correlations, and understand
people's satisfaction with what they are discussing. As one showcase, in this
paper, we summarize the data set of Twitter messages related to recent
demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights
from Twitter's data. Our proposed system automatically extracts the popular
latent topics in conversations regarding demonetization discussed in Twitter
via the Latent Dirichlet Allocation (LDA) based topic model and also identifies
the correlated topics across different categories. Additionally, it also
discovers people's opinions expressed through their tweets related to the event
under consideration via the emotion analyzer. The system also employs an
intuitive and informative visualization to show the uncovered insight.
Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI),
to select the best LDA models. The obtained LDA results show that the tool can
be effectively used to extract discussion topics and summarize them for further
manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with
arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by
other author
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
As of February 2016 Facebook allows users to express their experienced
emotions about a post by using five so-called `reactions'. This research paper
proposes and evaluates alternative methods for predicting these reactions to
user posts on public pages of firms/companies (like supermarket chains). For
this purpose, we collected posts (and their reactions) from Facebook pages of
large supermarket chains and constructed a dataset which is available for other
researches. In order to predict the distribution of reactions of a new post,
neural network architectures (convolutional and recurrent neural networks) were
tested using pretrained word embeddings. Results of the neural networks were
improved by introducing a bootstrapping approach for sentiment and emotion
mining on the comments for each post. The final model (a combination of neural
network and a baseline emotion miner) is able to predict the reaction
distribution on Facebook posts with a mean squared error (or misclassification
rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset:
https://github.com/jerryspan/FacebookR
Social media and sentiment in bioenergy consultation
Purpose: The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects.
Design/methodology/approach: This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’
Findings: Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results.
Research limitations/implications: Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector.
Originality/value: Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity
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