MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Zhang, Yaocheng; Ren, Wei; Zhu, Tianqing; Faith, Ehoche

doi:10.3390/sym11010115

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…Finally, compared with the results shown in our previous work [16], the result using the mix dictionary is worse than the previous result. After analysis of those two different results, we can guess that the new mix dictionary contains many new words that may reflect the detected results, while the manual judgement may also have contained some errors leading to the result being worse than before.…”

Section: Analysis Of the Comment's Sentiment Scorecontrasting

confidence: 60%

“…Note: This paper is an extended version of our paper published in Proceedings of the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), pp. 582-595, 2018 (Zhang et al, 2018) [16].…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we use a method based on the mixed sentiment dictionary, degree adverb dictionary, negative word dictionary and stop word dictionary. Moreover, in this paper, which is based on previous work [16], we have extended the HowNet-based sentiment dictionary. In brief e mix together some sentiment dictionaries from the Internet, and dynamically update the sentiment dictionary when the system detects some words with new meaning.…”

Section: Sentiment Analysis Methodsmentioning

confidence: 99%

“…In our previous work [16], we presented our comparison results by using the proposed method manually. In the present, we propose a stronger method.…”

Section: Analysis Of the Comment's Sentiment Scorementioning

confidence: 99%

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MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Zhang

Ren²,

Zhu

et al. 2018

Algorithms and Architectures for Parallel Processing

Self Cite

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The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments' sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

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Section: Analysis Of the Comment's Sentiment Scorecontrasting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Sentiment Analysis Methodsmentioning

confidence: 99%

“…In our previous work [16], we presented our comparison results by using the proposed method manually. In the present, we propose a stronger method.…”

Section: Analysis Of the Comment's Sentiment Scorementioning

confidence: 99%

See 2 more Smart Citations

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Zhang

Ren²,

Zhu

et al. 2018

Algorithms and Architectures for Parallel Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Affect recognition has been an active topic of research for the last two decades, and attempts have been made to detect emotions from many different sources of information, including text [1], facial expressions [2], speech [3], physiological signals [4,5,6] or interaction data [7], among others.…”

Section: Introductionmentioning

confidence: 99%

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

Sensors

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Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

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Big data and sentiment analysis: A comprehensive and systematic literature review

Hajiali

2020

Concurrency and Computation

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SummarySentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity. Moreover, big data is produced via mobile networks and social media. Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment. Accordingly, performing sentiment analysis on big data can be helpful for a business to take useful commercial insights from text‐oriented content. However, there are very few comprehensive investigations and profound argument in this context. The goal of this paper is to provide a comprehensive and systematic investigation of the state‐of‐the‐art techniques and highlight the directions for future research. In this paper, we used systematic literature review method and in the first step, we obtained 15 351 articles; then, based on different filters, 48 related articles were attained. We have selected 23 articles based on the year of publication, the relevance of the journal, the completeness of the text, the nonrepeatability of the title, and the page number. Also, we have categorized big data and sentiment analysis into two classifications: centralized and distributed platforms. Furthermore, the disadvantages and advantages of the investigated techniques are studied and their key issues are emphasized. Consequently, this study shows that a better analysis of textual big data in terms of sentiment increases efficiency, flexibility, and intelligence. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better handling of big data in the field of sentiment analysis. This study sheds some new light on using sentiment analysis and big data for public opinion estimation and prediction.

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MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Cited by 9 publications

References 8 publications

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Big data and sentiment analysis: A comprehensive and systematic literature review

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