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Analysing and Visualizing Tweets for U.S. President Popularity

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@article{IJASEIT8284,
   author = {Ernesto De Luca and Francesca Fallucchi and Romeo Giuliano and Giuseppe Incarnato and Franco Mazzenga},
   title = {Analysing and Visualizing Tweets for U.S. President Popularity},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {2},
   year = {2019},
   pages = {692--699},
   keywords = {Twitter; Russiagate; sentiment analysis; U.S. President popularity.},
   abstract = {

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8284},    doi = {10.18517/ijaseit.9.2.8284} }

EndNote

%A De Luca, Ernesto
%A Fallucchi, Francesca
%A Giuliano, Romeo
%A Incarnato, Giuseppe
%A Mazzenga, Franco
%D 2019
%T Analysing and Visualizing Tweets for U.S. President Popularity
%B 2019
%9 Twitter; Russiagate; sentiment analysis; U.S. President popularity.
%! Analysing and Visualizing Tweets for U.S. President Popularity
%K Twitter; Russiagate; sentiment analysis; U.S. President popularity.
%X 

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8284 %R doi:10.18517/ijaseit.9.2.8284 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 2 %@ 2088-5334

IEEE

Ernesto De Luca,Francesca Fallucchi,Romeo Giuliano,Giuseppe Incarnato and Franco Mazzenga,"Analysing and Visualizing Tweets for U.S. President Popularity," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 2, pp. 692-699, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.2.8284.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - De Luca, Ernesto
AU  - Fallucchi, Francesca
AU  - Giuliano, Romeo
AU  - Incarnato, Giuseppe
AU  - Mazzenga, Franco
PY  - 2019
TI  - Analysing and Visualizing Tweets for U.S. President Popularity
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 2
Y2  - 2019
SP  - 692
EP  - 699
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Twitter; Russiagate; sentiment analysis; U.S. President popularity.
N2  - 

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8284 DO - 10.18517/ijaseit.9.2.8284

RefWorks

RT Journal Article
ID 8284
A1 De Luca, Ernesto
A1 Fallucchi, Francesca
A1 Giuliano, Romeo
A1 Incarnato, Giuseppe
A1 Mazzenga, Franco
T1 Analysing and Visualizing Tweets for U.S. President Popularity
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 2
YR 2019
SP 692
OP 699
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Twitter; Russiagate; sentiment analysis; U.S. President popularity.
AB 

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8284 DO - 10.18517/ijaseit.9.2.8284