Moving Beyond Sentiment Analysis

2018 ◽  
pp. 349-377
Author(s):  
Jaime E. Settle
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Political Communication ◽  
Emotional Response ◽  
Opinion Leadership ◽  
Data Driven ◽  
Communication Analysis ◽  
Social Media Data ◽  
Face To Face ◽  
Media Data

While social media data present a plethora of new opportunities to study interpersonal political communication in ways often not feasible in the study of face-to-face conversations, the vast majority of research to date addresses content solely via data-driven sentiment analysis instead of theory-driven political communication analysis. This chapter argues that a fruitful path forward using textual social media data is to think more seriously about what can be learned broadly about the processes of interpersonal political communication, both online and offline. Social media data can be used to better understand phenomena about which a considerable amount is known, such as opinion leadership and emotional response to elite political communication. These data are also uniquely suited to study phenomena that have been previously unexplored because of a lack of suitable information, such as the operation of emotion in interpersonal political communication.

Comparison of different neural networks in sentiment analysis of social media data

2021 ◽  
Author(s):  
Vadim Moshkin ◽  
Andrew Konstantinov ◽  
Nadezhda Yarushkina ◽  
Alexander Dyrnochkin
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Media Data

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

Product Review Analysis Using Social Media Data Based on Sentiment Analysis

2020 ◽  
pp. 527-533
Author(s):  
S. M. Mazharul Hoque Chowdhury ◽  
Sheikh Abujar ◽  
Ohidujjaman ◽  
Khalid Been Md. Badruzzaman ◽  
Syed Akhter Hossain
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Product Review ◽  
Social Media Data ◽  
Review Analysis ◽  
Media Data

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

2017 ◽  
pp. 175-244
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
Public Issues ◽  
Public Events ◽  
Brand Reputation ◽  
Text Corpora ◽  
Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

scholarly journals Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 307
Author(s):  
Li Zhang ◽  
Haimeng Fan ◽  
Chengxia Peng ◽  
Guozheng Rao ◽  
Qing Cong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Hpv Vaccination ◽  
Experimental Results ◽  
Fine Tuning ◽  
Hpv Vaccines ◽  
Social Media Data ◽  
Public Sentiment ◽  
Media Data

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

The Role of Smart Technologies to Support Citizen Engagement and Decision Making

2018 ◽  
Vol 14 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Gabriela Viale Pereira ◽  
Gregor Eibl ◽  
Constantinos Stylianou ◽  
Gilberto Martínez ◽  
Haris Neophytou ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Life ◽  
Digital Technologies ◽  
Fuzzy Cognitive Maps ◽  
Data Driven ◽  
Social Media Data ◽  
Life Problems ◽  
Smart Technologies ◽  
Media Data

Smart government relies both on the application of digital technologies to enable citizen's participation in order to achieve a high level of citizen centricity and on data-driven decision making in order to improve the quality of life of citizens. Data-driven decisions in turn depend on accessible and reliable datasets, which open government and social media data are likely to promise. The SmartGov project uses digital technologies by integrating open and social media data in Fuzzy Cognitive Maps to model real life problems and simulate different scenarios leading to better decision making. This research performed a multiple-case analysis in two pilot cities. Both municipalities use the technologies to find the best routes: Limassol to improve the garbage collection and Quart de Poblet to improve the walking routes of chaperones guiding children to school. The article proposes a generic framework for Smart City Governance focusing on the inputs and outcomes of this process in the use of technologies for policy making built based on the analysis of the SmartGov.

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