3,005 research outputs found
Role of sentiment classification in sentiment analysis: a survey
Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readers’ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams
The proliferation of smartphones and wearable devices has increased the
availability of large amounts of geospatial streams to provide significant
automated discovery of knowledge in pervasive environments, but most prominent
information related to altering interests have not yet adequately capitalized.
In this paper, we provide a novel algorithm to exploit the dynamic fluctuations
in user's point-of-interest while forecasting the future place of visit with
fine granularity. Our proposed algorithm is based on the dynamic formation of
collective personality communities using different languages, opinions,
geographical and temporal distributions for finding out optimized equivalent
content. We performed extensive empirical experiments involving, real-time
streams derived from 0.6 million stream tuples of micro-blog comprising 1945
social person fusion with graph algorithm and feed-forward neural network model
as a predictive classification model. Lastly, The framework achieves 62.10%
mean average precision on 1,20,000 embeddings on unlabeled users and
surprisingly 85.92% increment on the state-of-the-art approach.Comment: Accepted as a full paper in the 2nd International Workshop on Social
Computing co-located with ICDM, 2018 Singapor
Multi-label prediction for political text-as-data
Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current "best practice"of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations among one's multiple labels are low
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