Research on the Construction of Sentiment Dictionary Based on Word2vec

Song, Xiaoyu; Zhao, Yang; Jin, Li-ting; Sun, Yue; Liu, Tong

doi:10.1145/3302425.3302481

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Word2Vec is one of the most popular fields in NLP [10][11][12]. In Research on the Construction of Sentiment Dictionary Based on Word2vec [13], SO-PMI algorithm was used to judge the emotional state of the words not recorded in the dictionary, and word2Vec algorithm was used to correct them. Finally, the corrected words were added to the dictionary to complete the reconstruction of the dictionary.…”

Section: Feature Vectorizationmentioning

confidence: 99%

WebShell Attack Detection Based on a Deep Super Learner

Luktarhan

Zhou

et al. 2020

Symmetry

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WebShell is a common network backdoor attack that is characterized by high concealment and great harm. However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. First, the collected data are deduplicated to prevent the influence of duplicate data on the result. Second, to detect the results of the algorithm, static and dynamic feature are taken as the feature of the algorithm to construct a comprehensive feature set. We then use the Word2Vec algorithm to vectorize the features. During this period, to prevent the outbreak of the number of features, we use a genetic algorithm to extract the validity of the feature dimension. Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved.

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Section: Feature Vectorizationmentioning

confidence: 99%

WebShell Attack Detection Based on a Deep Super Learner

Luktarhan

Zhou

et al. 2020

Symmetry

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“…Among them, the grounded theory provided us with emotion categories and corresponding seed words. Calculating word vector similarity, widely used in NLP [75], helped us automatically search similar words and expand the lexicon. And a manual inspection approach corrected errors of machine expansion.…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

“…Word2Vec is an open source tool, which can train a set of word vectors from massive corpus (1.01 million Weibo texts in this study) through a neural network model [75]. The core function parameters set in this study were as follows: sg = 0, size = 200, window =5, and min_count = 1e-3.…”

Section: Words Expansionmentioning

confidence: 99%

A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

Jiang

et al. 2021

IEEE Access

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As one of the most popular social media platforms in China, Weibo has aggregated huge numbers of texts containing people's thoughts, feelings, and experiences. Analyzing emotions expressed on Weibo has attracted a great deal of academic attention. Emotion lexicon is a vital foundation of sentiment analysis, but the existing lexicons still have defects such as a limited variety of emotions, poor crossscenario adaptability, and confusing written and online expressions and words. By combining grounded theory and semi-automatic methods, we built a Weibo-based emotion lexicon for sentiment analysis. We first took a bottom-up approach to derive a theoretical model for emotions expressed on Weibo, and the substantive coding led to eight core emotion categories: joy, expectation, love, anger, anxiety, disgust, sadness, and surprise. Second, we built a new emotion lexicon containing 2,964 words by manually selecting seed words, constructing a word vector model to expand words, and making rules to filter words. Finally, we tested the effectiveness of our lexicon by using a lexicon-based approach to recognize the emotions expressed in Weibo text. The results showed that our lexicon performed better in Weibo emotion recognition than five other Chinese emotion lexicons. This study proposed a method to construct an emotion lexicon that considered both theory and application by combining qualitative research and artificial intelligence methods. Our work also provided a reference for future research in the field of social media sentiment analysis.

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“…There are two main approaches for automatic construction of sentiment dictionaries: the semantic-dictionary-based approach and the corpus-based approach [31]. Tan used the HowNet sentiment dictionary as a basis and added the commonly used web terms from current dictionaries to construct a microblog emotion dictionary [32].…”

Section: The Sentiment-dictionary-based Methodsmentioning

confidence: 99%

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

Duan

Shen

Luo

2022

IJERPH

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As the relationship between the built environment and the sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space and time and improving the quality of urban construction from the perspective of public emotion and mental health. While youth is a powerful force in urban construction, there are no studies on the relationship between urban youth sentiments and the built environment. With the development of the Internet, social media has provided a large source of data for the metrics of youth sentiment. Based on data from more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 to 25 July 2021) in Shanghai and using a machine learning algorithm for attention mechanism, this study calculates the sentiment label and sentiment intensity of each comment. Ten elements in five aspects were selected to assess the built environment at different scales and also to explore the correlations between built environment elements and sentiment intensity at different scales. The study finds that the overall sentiment of Shanghai youth tends to be negative. Sentiment intensity is significantly associated with most built environment elements at smaller scales. Urban youth have a higher proportion of both happy and sad sentiments, within which sad sentiments are more closely related to the built environment and are significantly related to all built environment elements. This study uses a deep learning algorithm to improve the accuracy of sentiment classification and confirms that the built environment has a great impact on sentiment. This research can help cities develop built environment optimization measures and policies to create positive emotional environments and enhance the well-being of urban youth.

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Research on the Construction of Sentiment Dictionary Based on Word2vec

Cited by 5 publications

References 4 publications

WebShell Attack Detection Based on a Deep Super Learner

WebShell Attack Detection Based on a Deep Super Learner

A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

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