Research on Sentiment Analysis of Multiple Classifiers Based on Word2vec

Ge, Xingtong; Jin, Xiaofang; Xu, Ying

doi:10.1109/ihmsc.2018.10159

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The proposed approach has been implemented on servers and evaluated on multiple devices. The result shows the proposed approach and showed high accuracy According to a study by [30] has also performed quantitative analysis for sentiment analysis. Word2vec method has been used for extracting the features, and then Principal Component Analysis (PCA) was used to identify the important elements and structure.…”

Section: Literature Reviewmentioning

confidence: 90%

Survey Analysis: Enhancing the Security of Vectorization by Using word2vec and CryptDB

Yousuf¹,

Salloum²

2020

Adv. sci. technol. eng. syst. j.

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Vectorization is extracting data from strings through Natural Language Processing by using different approaches; one of the best approaches used in vectorization is word2vec. To make the vectorized data secure, we must apply a security method, which will be CryptDB. The paper is analyzing the survey, which is created to interview security engineers through the SPSS tool. By analyzing the responses from software security engineers, it is seen that both word2vec and CryptDB works significantly. Word2vec is an effective vectorization approach, while CryptDB is an effective, secure database. In future work, we will be developing a secure vectorization using both approaches.

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Section: Literature Reviewmentioning

confidence: 90%

Survey Analysis: Enhancing the Security of Vectorization by Using word2vec and CryptDB

Yousuf¹,

Salloum²

2020

Adv. sci. technol. eng. syst. j.

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“…The technique we used in the lexicon-based approach is explained in detail in Section III-C. Many previous studies have tried to employ wordembedding methods for machine leaning or deep learningbased sentiment classification [15], [12], [13]. In our prediction model we experiment with two different word embedding methods: Word2Vec and FastText.…”

Section: B Proposed Sentiment Analysis Architecturementioning

confidence: 99%

“…Furthermore, in recent works, word embedding-based method are applied for sentiment classification. A few have used Word2Vec embeddings [12], [13]. Deep learning has emerged as a powerful machine learning technique and is also popularly used in sentiment analysis in recent years [14], [15].…”

Section: Literature Surveymentioning

confidence: 99%

Uncovering Flaming Events on News Media in Social Media

Rajapaksha¹,

Farahbakhsh²,

Crespi³

et al. 2019

2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC)

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Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.

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Emotion Classification and Intensity Prediction using Hybrid Deep Learning Models

Jain

Parmar

2022

2022 International Conference on Edge Computing and Applications (ICECAA)

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Research on Sentiment Analysis of Multiple Classifiers Based on Word2vec

Cited by 3 publications

References 12 publications

Survey Analysis: Enhancing the Security of Vectorization by Using word2vec and CryptDB

Survey Analysis: Enhancing the Security of Vectorization by Using word2vec and CryptDB

Uncovering Flaming Events on News Media in Social Media

Emotion Classification and Intensity Prediction using Hybrid Deep Learning Models

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