A Brief Review on the Use of Sentiment Analysis Approaches in Social Networks

Ramírez-Tinoco, Francisco Javier; Alor-Hernández, Giner; Sánchez-Cervantes, José Luis; Zepahua, Beatriz Alejandra Olivares; Rodríguez-Mazahua, Lisbeth

doi:10.1007/978-3-319-69341-5_24

Cited by 12 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, an early detection of depression system can be built [ 36 ], or an early detection system of bipolar behavior. In [ 31 ], the authors stated that most sentiment analysis studies are focused on polarity, but subjectivity is also very important to detect “private states” (opinions, emotions, sentiments, beliefs, and speculations) [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…Sentiment analysis of the content was performed using the Python library TextBlob. In sentiment analysis, both the polarity and the subjectivity were analyzed, the former being one of the most popular measures [ 31 ], although the latter is supposed to be more useful for analyzing emotions. To perform sentiment analysis, TextBlob Python library was used after translating text to English.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

Subirats

Reguera

Bañón

et al. 2018

IJERPH

View full text Add to dashboard Cite

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

Subirats

Reguera

Bañón

et al. 2018

IJERPH

View full text Add to dashboard Cite

show abstract

“…The Review Mining phase (RM) phase is also called sentiment analysis. Sentiment analysis includes various tasks such as detection of subjectivity, polarity, sentiment magnitude, and type of emotion (Ramírez-Tinoco et al 2017). Subjectivity detection is the subtask wherein the text gets classified as subjective (positive or negative sentiment) or objective (neutral, factual, or without any sentiment).…”

Section: Introductionmentioning

confidence: 99%

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Kaur

Sharma

2022

J Ambient Intell Human Comput

View full text Add to dashboard Cite

The reviews posted online by the end-users can help the business owners obtain a fair evaluation of their products/services and take the necessary steps. However, due to the large volume of online reviews being generated from time to time, it becomes challenging for business owners to track each review. The Customer Review Summarization (CRS) model that can present the summarized information and offer businesses with significant acumens to understand the reason behind customers' choices and behavior, would therefore be desirable. We propose the Hybrid Analysis of Sentiments (HAS) for the perspective of effective CRS in this paper. The HAS consists of steps like pre-processing, feature extraction, and review classification. The pre-processing phase removes the unwanted data from the text reviews using Natural Language Processing (NLP) based on different pre-processing functions. For efficient feature extraction, the hybrid mechanism consisting of aspect-related features and review-related features is proposed to build the unique feature vector for each customer review. Review classification is performed using different supervised classifiers like Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The experimental results show that HAS efficiently performed the sentiment analysis and outperformed the existing state-of-the-art techniques with an F1 score of 92.2%.

show abstract

“…Information is sent digitally from user to user in today's fast-paced environment, which might affect how other users interpret a specific occurrence (McGregor, 2019). According to Ramírez-Tinoco et al (2018), sentiment analysis is a natural language processing method that may be used to evaluate and grasp sentiments and emotions. Opinions, both positive and negative, and neutral, are discussed (Medhat et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Muhammad

Nat

2022

InformingSciJ

View full text Add to dashboard Cite

Aim/Purpose: Nigeria’s university education goes through incessant strikes by the Academic Staff Union of Universities (ASUU). This strike has led to shared emotion on micro-blogging sites like Twitter. This study analyzed selected historical tweets from the “ASUU” to understand citizens’ opinions. Background: The researchers conducted sentiment analysis and topic modelling to understand Twitter users’ opinions on the strike. Methodology: The researchers used the Valence Aware Dictionary for Sentiment Reasoning (VADER) technique for sentiment analysis, and the Latent Dirichlet allocation (LDA) was used for topic modelling. A total of 10,000 tweets were first extracted for the study. After data cleaning, 1323 tweets were left. Contribution: To the researcher’s best knowledge, no published study has presented a sentiment analysis on the topic of the ASUU strike using the Twitter dataset. This research will fill this gap by providing a sentiment analysis and drawing out subjects by exploring the tweets on the phrase “ASUU.” Findings: The sentiment analysis result using VADER returned 567 tweets as ‘Negative,’ with the remaining 544 and 212 categorized as Positive and Neutral. The result of the LDA returned six topics, all comprising seven keywords. The topics were the solution to the strike, ASUU strike effect, strike Call-off, appeal to ASUU, student protest and student appeal. Recommendation for Researchers: Researchers can use this study’s findings to compare with other contexts of opinion mining. Practitioners may also use the research to understand better the attitudes of their staff and students about the strikes to create actionable solutions before the suspension of the strike. Future Research: Future studies can collect information from other social networking and blogging sites.

show abstract

A Brief Review on the Use of Sentiment Analysis Approaches in Social Networks

Cited by 12 publications

References 23 publications

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Contact Info

Product

Resources

About