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

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.

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

scholarly journals Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

2020 ◽  
Vol 14 (4) ◽  
pp. 417
Author(s):  
Puji Winar Cahyo ◽  
Muhammad Habibi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Online News ◽  
Support Vector ◽  
Media Content ◽  
Positive Sentiment ◽  
Negative Sentiment

The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.

scholarly journals A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content

2017 ◽  
Author(s):  
Tushar Maheshwari ◽  
Aishwarya N. Reganti ◽  
Samiksha Gupta ◽  
Anupam Jamatia ◽  
Upendra Kumar ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Content
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