Sentiment analysis of arabic social media content: a comparative study

2013 ◽  
Author(s):  
Rawan T. Khasawneh ◽  
Heider A. Wahsheh ◽  
Mohammed N. Al-Kabi ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Media Content

iSA: A fast, scalable and accurate algorithm for sentiment analysis of social media content

2016 ◽  
Vol 367-368 ◽  
pp. 105-124 ◽  
Author(s):  
Andrea Ceron ◽  
Luigi Curini ◽  
Stefano Maria Iacus
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Content

scholarly journals Sentiment Analysis of Social Media Content for Music Recommendation

2021 ◽  
Vol 4 (1) ◽  
pp. 110-120
Author(s):  
S Akuma ◽  
P Obilikwu ◽  
E Ahar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Recommender System ◽  
Emotional State ◽  
Media Content ◽  
Random Testing ◽  
Music Recommendation ◽  
Social Media Platforms ◽  
High Predictability ◽  
Use Of Social Media

There is a growing use of social media for communication and entertainment. The information obtained from these social media platforms like Facebook, Linkedln, Twitter and so on can be used for inferring users’ emotional state. Users express their emotions on social media such as Twitter through text and emojis. Such expression can be harvested for the development of a recommender system. In this work, live tweets of users were harvested for the development of an emotion-based music recommender system. The emotions captured in this work include happy, fear, angry disgusted and sad. Users tweets in the form of emojis or text were matched with predefined variables to predict the emotion of users. Random testing of live tweets using the system was conducted and the result showed high predictability.

scholarly journals Narratives of the Refugee Crisis: A Comparative Study of Mainstream-Media and Twitter

2019 ◽  
Vol 7 (2) ◽  
pp. 275-288 ◽  
Author(s):  
Adina Nerghes ◽  
Ju-Sung Lee
Keyword(s):  
Social Media ◽  
Comparative Study ◽  
Topic Modeling ◽  
Media Content ◽  
Refugee Crisis ◽  
Mainstream Media ◽  
Raising Awareness ◽  
Media Space ◽  
European Refugee Crisis ◽  
Media Form

The European refugee crisis received heightened attention at the beginning of September 2015, when images of the drowned child, Aylan Kurdi, surfaced across mainstream and social media. While the flows of displaced persons, especially from the Middle East into Europe, had been ongoing until that date, this event and its coverage sparked a media firestorm. Mainstream-media content plays a major role in shaping discourse about events such as the refugee crisis, while social media’s participatory affordances allow for the narratives to be perpetuated, challenged, and injected with new perspectives. In this study, the perspectives and narratives of the refugee crisis from the mainstream news and Twitter—in the days following Aylan’s death—are compared and contrasted. Themes are extracted through topic modeling (LDA) and reveal how news and Twitter converge and also diverge. We show that in the initial stages of a crisis and following the tragic death of Aylan, public discussion on Twitter was highly positive. Unlike the mainstream-media, Twitter offered an alternative and multifaceted narrative, not bound by geo-politics, raising awareness and calling for solidarity and empathy towards those affected. This study demonstrates how mainstream and social media form a new and complementary media space, where narratives are created and transformed.

SACI: Sentiment Analysis by Collective Inspection on Social Media Content

2015 ◽  
Author(s):  
Leonardo Rocha ◽  
Fernando Mourro ◽  
Thiago Silveira ◽  
Rodrigo Chaves ◽  
Giovanni Sa ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Content

SACI: Sentiment analysis by collective inspection on social media content

2015 ◽  
Vol 34 ◽  
pp. 27-39 ◽  
Author(s):  
Leonardo Rocha ◽  
Fernando Mourão ◽  
Thiago Silveira ◽  
Rodrigo Chaves ◽  
Giovanni Sá ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Content

scholarly journals A Comparative Study of Different Classification Techniques for Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 49-57
Author(s):  
Soumadip Ghosh ◽  
Arnab Hazra ◽  
Abhishek Raj
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Product Reviews ◽  
Accurate Model

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.

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