National happiness index monitoring using Twitter for bilanguages

Wang, Di; Alrubaie, Ahmad; Hirsch, Benjamin; Pole, Gregory Cameron

doi:10.1007/s13278-021-00728-0

Cited by 19 publications

(14 citation statements)

References 36 publications

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“…Wang et al ( 2021 ) proposed a system for general population sentiment monitoring from a social media stream (Twitter), through comprehensive multilevel filters, and improved latent Dirichlet allocation (LDA) method for sentiment classification. They reached an accuracy of 68% for general sentiment analysis using real-world content.…”

Section: Related Workmentioning

confidence: 99%

A reliable sentiment analysis for classification of tweets in social networks

AminiMotlagh

Shahhoseini

Fatehi

2022

Soc. Netw. Anal. Min.

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In modern society, the use of social networks is more than ever and they have become the most popular medium for daily communications. Twitter is a social network where users are able to share their daily emotions and opinions with tweets. Sentiment analysis is a method to identify these emotions and determine whether a text is positive, negative, or neutral. In this article, we apply four widely used data mining classifiers, namely K-nearest neighbor, decision tree, support vector machine, and naive Bayes, to analyze the sentiment of the tweets. The analysis is performed on two datasets: first, a dataset with two classes (positive and negative) and then a three-class dataset (positive, negative and neutral). Furthermore, we utilize two ensemble methods to decrease variance and bias of the learning algorithms and subsequently increase the reliability. Also, we have divided the dataset into two parts: training set and testing set with different percentages of data to show the best train–test split ratio. Our results show that support vector machine demonstrates better outcomes compared to other algorithms, showing an improvement of 3.53% on dataset with two-class data and 7.41% on dataset with three-class data in accuracy rate compared to other algorithms. The experiments show that the accuracy of single classifiers slightly outperforms that of ensemble methods; however, they propose more reliable learning models. Results also demonstrate that using 50% of the dataset as training data has almost the same results as 70%, while using tenfold cross-validation can reach better results.

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Section: Related Workmentioning

confidence: 99%

A reliable sentiment analysis for classification of tweets in social networks

AminiMotlagh

Shahhoseini

Fatehi

2022

Soc. Netw. Anal. Min.

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“…The Sanders [35,36] [N2-N4] dataset is manually labelled by one annotator and consists of 5512 tweets. We have used 4410 tweets for training and 1102 tweets for testing purposes.…”

Section: Sandersmentioning

confidence: 99%

A Feature-Based Approach for Sentiment Quantification Using Machine Learning

et al. 2022

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Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantification, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantification when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantification, including polling, trend analysis, automatic summarization, and rumor or fake news detection.

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“…Social media today help to find out first-hand the thoughts, feelings and concerns of people, both locally and internationally. A study conducted by Di Wang, Ahmad Al-Rubaie, Benjamin Hirsch and Gregory Cameron Pole, for example, proposes a general system for population happiness index monitoring using sentiment analysis from a social media stream (Twitter) through comprehensive multi-level filters, which is of great relevance not only during the pandemic [9]. Emotion identification enables people to understand better the population sentiment toward particular events [10].…”

Section: Research Areamentioning

confidence: 99%

Socio-cultural changes influenced by information and communication technology

Petrova

Pervukhina

2022

E3S Web Conf.

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Information and communication technologies have eliminated communication barriers, transformed forms of communication into social sustainability, created a global virtual community with its own language and culture. The list of Internet phenomena, which includes elements of a mass culture modified by technology, is long indeed. It is apparent that social media platforms, being part of the innovation process in modern society, have had and continue to have an impact on people's identity, especially on the cultural identity and emotional tendencies of young people. The objective of this study is to consider these ongoing changes influenced by information and communication technologies. To achieve the objective, the hypothesis is put forward that information and communication technologies contribute to satisfying the need for communication and obtaining information, create the language picture of the virtual world on a global scale, have a significant impact on the emotional component and cultural universals of young people. The study consists of two parts. The first part is a systematic review of the scientific literature focused on social media platforms and the influence of such dynamic parameters as speed and scale of interaction on socio-cultural changes, emotional tendencies, attitudes and behavior of representatives of youth culture. The second part contains a logical conclusion on socio-psychological changes based on the results of a survey aimed at collecting the data necessary to analyse the use of social media technologies and to identify dominant platforms in the youth environment.

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National happiness index monitoring using Twitter for bilanguages

Cited by 19 publications

References 36 publications

A reliable sentiment analysis for classification of tweets in social networks

A reliable sentiment analysis for classification of tweets in social networks

A Feature-Based Approach for Sentiment Quantification Using Machine Learning

Socio-cultural changes influenced by information and communication technology

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