A Survey of Sentiment Analysis Based on Machine Learning

Lin, Pingping; Luo, Xudong

doi:10.1007/978-3-030-60450-9_30

Cited by 17 publications

(13 citation statements)

References 35 publications

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“…Sentiment analysis is the classification of emotions and attitudes in subjective texts, and the main methods are machine learning and deep learning. From the perspective of machine learning, the text sentiment analysis method based on machine learning (Lin and Luo, 2020) needs to use a corpus to train a classification model. For example, Yan et al (2020) One of the major breakthroughs in the history of natural language processing is the attention mechanism.…”

Section: Related Work Neural Language Model For Nlpmentioning

confidence: 99%

Text Sentiment Analysis Based on Transformer and Augmentation

et al. 2022

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With the development of Internet technology, social media platforms have become an indispensable part of people’s lives, and social media have been integrated into people’s life, study, and work. On various forums, such as Taobao and Weibo, a large number of people’s footprints are left all the time. It is these chats, comments, and other remarks with people’s emotional evaluations that make up part of public opinion. Analysis of this network public opinion is conducive to maintaining the peaceful development of society. Therefore, sentiment analysis has become a hot research field and has made great strides as one of the hot topics in the field of natural language processing. Currently, the BERT model and its variants have achieved excellent results in the field of NLP. However, these models cannot be widely used due to huge demands on computing resources. Therefore, this paper proposes a model based on the transformer mechanism, which mainly includes two parts: knowledge distillation and text augmentation. The former is mainly used to reduce the number of parameters of the model, reducing the computational cost and training time of the model, and the latter is mainly used to expand the task text so that the model can achieve excellent results in the few-sample sentiment analysis task. Experiments show that our model achieves competitive results.

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Section: Related Work Neural Language Model For Nlpmentioning

confidence: 99%

Text Sentiment Analysis Based on Transformer and Augmentation

et al. 2022

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“…Future research might nonetheless explore the relevance of a different approach to this measurement problem: train a dedicated machine learning algorithm on human-rated data along each dimension. This would have the benefit of specificity, but is not without costs and challenges (see, e.g., Lin and Luo (2020) or Ram and Nagappan (2018) for a discussion in the context of sentiment analysis).…”

Section: Measures Of Code Qualitymentioning

confidence: 99%

Follow the Leader: Technical and Inspirational Leadership in Open Source Software

Hergueux,

Kessler

2022

Preprint

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We conduct the first comprehensive study of the behavioral factors which predict leader emergence within open source software (OSS) virtual teams. We leverage the full history of developers' interactions with their teammates and projects at github.com between January 2010 and April 2017 (representing about 133 million interactions) to establish that -contrary to a common narrative describing open source as a pure "technical meritocracy" -developers' communication abilities and community building skills are significant predictors of whether they emerge as team leaders. Inspirational communication therefore appears as central to the process of leader emergence in virtual teams, even in a setting like OSS, where technical contributions have often been conceptualized as the sole pathway to gaining community recognition.Those results should be of interest to researchers and practitioners theorizing about OSS in particular and, more generally, leadership in geographically dispersed virtual teams, as well as to online community managers.

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“…To address the problems of sentiment analysis, previously, approaches based on machine learning algorithms and the sentiment lexicon have been used. However, these methods have limitations such as limited data, word order and a large number of tagged texts that make them ineffective for NLP tasks [45]. However, for some of these problems, models based on deep learning have been the solution, these methods have been gaining popularity, thus proving to be a better option to face the problem of sentiment analysis and this is attributed to the high performance they show in different tasks of the NLP [46].…”

Section: Deep Learning For Natural Language Processing and Sentiment Analysismentioning

confidence: 99%

Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

et al. 2021

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The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

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A Survey of Sentiment Analysis Based on Machine Learning

Cited by 17 publications

References 35 publications

Text Sentiment Analysis Based on Transformer and Augmentation

Text Sentiment Analysis Based on Transformer and Augmentation

Follow the Leader: Technical and Inspirational Leadership in Open Source Software

Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

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