9 research outputs found
Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality
Matrix factorization (MF) is extensively used to mine the user preference
from explicit ratings in recommender systems. However, the reliability of
explicit ratings is not always consistent, because many factors may affect the
user's final evaluation on an item, including commercial advertising and a
friend's recommendation. Therefore, mining the reliable ratings of user is
critical to further improve the performance of the recommender system. In this
work, we analyze the deviation degree of each rating in overall rating
distribution of user and item, and propose the notion of user-based rating
centrality and item-based rating centrality, respectively. Moreover, based on
the rating centrality, we measure the reliability of each user rating and
provide an optimized matrix factorization recommendation algorithm.
Experimental results on two popular recommendation datasets reveal that our
method gets better performance compared with other matrix factorization
recommendation algorithms, especially on sparse datasets
The Effects of Twitter Sentiment on Stock Price Returns
Social media are increasingly reflecting and influencing behavior of other
complex systems. In this paper we investigate the relations between a well-know
micro-blogging platform Twitter and financial markets. In particular, we
consider, in a period of 15 months, the Twitter volume and sentiment about the
30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We
find a relatively low Pearson correlation and Granger causality between the
corresponding time series over the entire time period. However, we find a
significant dependence between the Twitter sentiment and abnormal returns
during the peaks of Twitter volume. This is valid not only for the expected
Twitter volume peaks (e.g., quarterly announcements), but also for peaks
corresponding to less obvious events. We formalize the procedure by adapting
the well-known "event study" from economics and finance to the analysis of
Twitter data. The procedure allows to automatically identify events as Twitter
volume peaks, to compute the prevailing sentiment (positive or negative)
expressed in tweets at these peaks, and finally to apply the "event study"
methodology to relate them to stock returns. We show that sentiment polarity of
Twitter peaks implies the direction of cumulative abnormal returns. The amount
of cumulative abnormal returns is relatively low (about 1-2%), but the
dependence is statistically significant for several days after the events
I Click, Therefore I am:Predicting Clicktivist-Like Actions on Candidates’ Facebook Posts During the 2016 US Primary Election
Facebook “likes” are often used as a proxy of users’ attention and an affirmation of what is posted on Facebook (Gerodimos & Justinussen, 2015). To determine what factors predict “likes,” the authors analyzed Facebook posts made by the campaigns of Hillary Clinton, Bernie Sanders, and Donald Trump, the top three candidates from the 2016 US primary election. Several possible factors were considered, such as the types of posts, the use of pronouns and emotions, the inclusion of slogans and hashtags, references made to opponents, as well as candidate’s mentions on national television. The results of an ordinary least-squared regression analysis showed that the use of highly charged (positive or negative) emotions and personalized posts (first-person singular pronouns) increased “likes” across all three candidates’ Facebook pages, whereas visual posts (posts containing either videos or photos) and the use of past tenses were liked more often by Hillary Clinton and Bernie Sanders’ followers than by Trump’s followers. Television mentions boosted likes on Clinton and Sanders’ posts but had a negative effect on Trump’s. The study contributes to the growing literature on digitally networked participation (Theocharis, 2015) and supports the emerging notion of the new “hybrid media” system (Chadwick, 2013) for political communication. The study also raises questions as to the relevance of platforms such as Facebook to deliberative democratic processes since Facebook users are not necessarily engaging with the content in an organic way, but instead might be guided to specific content by the Facebook timeline algorithm and targeted ads