Chinese Microblog Sentiment Analysis Based on Semi-supervised Learning

Zhu, Shaojie; Xu, Bing; Zheng, Dequan; Zhao, Tiejun

doi:10.1007/978-1-4614-6880-6_28

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…However, this approach is computationally expensive and has been tested only on English language data. The least accurate approaches of those that we considered were the ones proposed by Zhu et al [73], Habernal et al [23], and Mizumoto et al [34].…”

Section: Resultsmentioning

confidence: 99%

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

et al. 2016

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With the advent of the internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing state-of-the-art approaches on common data. Precision observed in our experiments is typically lower than that reported by the original authors, which we attribute to lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.The online version of the original article can be found under

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Section: Resultsmentioning

confidence: 99%

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

et al. 2016

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“…In [29], Sina microblog data is analyzed using SVM. The method saves more time and shows good performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Alhashmi

Khedr²,

Arif

et al. 2021

IEEE Access

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In this paper, sentiment analysis of two critical events is presented using machine learning (ML) techniques. COVID-19 has put immense pressure across the globe and sentiment analysis of data from Twitter using ML techniques has become a hot topic. We extract the COVID-19 and Expo2020 data from twitter. First, we evaluate the Twitter data of these two significant events for sentiment analysis and then use the classification algorithm to find out the usefulness of the proposed methodology. A hybrid approach that uses supervised learning model Support Vector Machine (SVM) combined with Bayes Factor Tree Augmented Naive Bayes (BFTAN) technique is proposed to accurately classify the input tweet while keeping in mind the different challenges of sentiment analysis. Our study has four main contributions: a) hybrid classification techniques are thoroughly explored for sentiment analysis, b) a novel hybrid classification approach is proposed for sentiment analysis, c) a new Twitter dataset related to COVID-19 that can be used for future research, d) empirical study to show that the hybrid-classification approach can achieve comparable performance in improving accuracy, identifying the polarity of comparative sentences, distinguishing the intensity of opinion words, considering negative words, and handling sarcasm as well. The experimental results show that the proposed approach is robust in producing correct classification results with the tradeoff of poor time efficiency. Also, the accuracy of the proposed model is comparable to other classifiers, which is encouraging. Class distribution of each dataset demonstrates that more than 60% of tweets are negative.

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“…Chinese 53 [474,289,150,286,122,415,95,199,546,90,218,57,339,424,496,347,200,106,107,355,312,439,364,139,174,207,14,134,98,135,73,520,166,74,176,510,492,386,389,78,390,493,392,394,189,396,397,470,414,398,501,…”

Section: Language Total Studiesmentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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Chinese Microblog Sentiment Analysis Based on Semi-supervised Learning

Cited by 12 publications

References 6 publications

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Over a Decade of Social Opinion Mining: A Systematic Review

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