Chinese sentiment classification using a neural network tool &amp;#x2014; Word2vec

Su, Zengcai; Xu, Hua; Zhang, Dongwen; Xu, Yong

doi:10.1109/mfi.2014.6997687

Cited by 35 publications

(18 citation statements)

References 17 publications

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“…Glove solves the problem that Word2Vec only considers words as well as local window information and ignores the statistical information of the corpus. With regarding, this study is demonstrated by skillfully using word embedding to transform news headlines into highdimensional vectors [20]. Secondly, the k-means clustering method is used to classify the news into K categories.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis for news and social media in COVID-19

Zhong

et al. 2020

Proceedings of the 6th ACM SIGSPATIAL International Workshop on Emergency Management Using GIS

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During the COVID-19 epidemic, the news is overwhelming in people's daily life. So, we aim to extract key information from a large amount of public news. This paper focus on the daily sentiment distribution of news and public opinion on Weibo that refers to the key word COVID-19. First, we refining the key news from all the news in a day to deal with long and large news data. Second, we transformer the headline into a highdimensional vector. And then, divided them into k categories on the strength of k-means clustering algorithm. Finally, choose the closet news to the mean vector as the key news of the day. Moreover, we conduct sentiment analysis on all key news and Weibo data. By comparing the sentiment trend of news and Weibo, this study provides a new channel to analyze social public opinion. CCS CONCEPTS • General and reference → Cross-computing tools and techniques → Empirical studies

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Section: Introductionmentioning

confidence: 99%

Sentiment analysis for news and social media in COVID-19

Zhong

et al. 2020

Proceedings of the 6th ACM SIGSPATIAL International Workshop on Emergency Management Using GIS

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“…Both studies used vector representation as the feature weighting method. Unlike another study [16], Word2vec was actually used for the feature selection process by clustering features based on similarities. Besides Word2vec, other researches [3], [8], [4] also used the TF-IDF method as the feature weighting process.…”

Section: Methodsmentioning

confidence: 99%

Development of Rule-Based Feature Extraction in Multi-label Text Classification

Mediamer¹,

Adiwijaya²,

Faraby³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Hadith is the second main guidelines after the Holy Quran in the Islamic religion, which was revealed through the Messenger of Allah. Today, Hadith can classified by more than one class such as advice class, prohibited, and information to facilitate readers of Hadith in filtering the appropriate classes for each Hadith of Rasulullah SAW. In the course of research, there are many kinds of data involved in a text classification study. Therefore, special handling that fit with the characteristics of certain data is required. This study investigates the handling of multi-label data-Hadith Bukhari in Indonesian translation-focusing on feature extraction, feature weighted, and preprocessing methods. This study uses a rule-based feature extraction combined with several types of preprocessing along with three types of feature-weighted methods: TF-IDF, Word2vec, and Word2vec weighted with TF-IDF, the five preprocessing stages in this research: Case Folding, Tokenization, Remove Punctuation, Stopword Removal, and Stemming. From the 13 experiments conducted in this study consist of 2000 hadiths, it was found that the best performance for multi-label classification of Hadith data produced by the combination of the proposed rule-based feature extraction, Word2vec feature weighted method, and without using Stemming and Stopword Removal in the preprocessing phase. The Hamming Loss value obtained from this combination was 0.0623. The results show that our rule-based feature extraction method better than baseline method.

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“…First, running the sigmoid layer which determined which cell would be the output, then place the cell state through the tanh and increased the output of the sigmoid gate, so that only the part we specified was the output. Calculation of output gate with equations (5) and (6).…”

Section: 3 Lstm Layermentioning

confidence: 99%

“…However, semantic features are rarely considered in sentiment classification. Semantic features can reveal deep and implicit semantic relationships between words which can be more useful in the classification of sentiments [6]. In this study, word2vec is used in the feature extraction process.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

Nurrohmat¹,

Sn²

2019

Indonesian J. Comput. Cybern. Syst.

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The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.

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Chinese sentiment classification using a neural network tool — Word2vec

Cited by 35 publications

References 17 publications

Sentiment analysis for news and social media in COVID-19

Sentiment analysis for news and social media in COVID-19

Development of Rule-Based Feature Extraction in Multi-label Text Classification

Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

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