Sentiment strength detection with a context-dependent lexicon-based convolutional neural network

Huang, Minghui; Xie, Haoran; Rao, Yanghui; Feng, Jingrong; Wang, Fu Lee

doi:10.1016/j.ins.2020.02.026

Cited by 46 publications

(13 citation statements)

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“…As future work, we plan to improve the accuracy of our classification by combining features form word embeddings and from term frequencies (or tf-idf weighting). Besides, as pointed out by other authors [14], [28], [40], the word embeddings approach can also become useful for the sentiment estimation. In the end, we plan to apply other supervised classification techniques, as well as to use this approach on other variables, since we are still collecting data as we write.…”

Section: Discussionmentioning

confidence: 90%

“…Other Italian lexicons could show a good level of agreement with one of the two used here. However, we believe that to obtain realistic and reliable results, it would be necessary to use a supervised learning method with human tagging [6] by labelling a sample of tweets or, at least, to use a (COVID-19) context-dependent lexicon [14], [3].…”

Section: Sentiment Analysismentioning

confidence: 99%

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COVID-19 Outbreak through Tweeters’ Words: Monitoring Italian Social Media Communication about COVID-19 with Text Mining and Word Embeddings

Sciandra

2020

2020 IEEE Symposium on Computers and Communications (ISCC)

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In this paper we aim to analyze the Italian social media communication about COVID-19 through a Twitter dataset collected in two months. The text corpus had been studied in terms of sensitivity to the social changes that are affecting people's lives in this crisis. In addition, the results of a sentiment analysis performed by two lexicons were compared and word embedding vectors were created from the available plain texts. Following we tested the informative effectiveness of word embeddings and compared them to a bag-of-words approach in terms of text classification accuracy. First results showed a certain potential of these textual data in the description of the different phases of the outbreak. However, a different strategy is needed for a more reliable sentiment labeling, as the results proposed by the two lexicons were discordant. Finally, although presenting interesting results in terms of semantic similarity, word embeddings did not show a predictive ability higher than the frequency vectors of the terms.

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Section: Discussionmentioning

confidence: 90%

Section: Sentiment Analysismentioning

confidence: 99%

COVID-19 Outbreak through Tweeters’ Words: Monitoring Italian Social Media Communication about COVID-19 with Text Mining and Word Embeddings

Sciandra

2020

2020 IEEE Symposium on Computers and Communications (ISCC)

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“…Experimental results of word similarity, word analogy reasoning, text classification, sentiment analysis, and case studies validate the effectiveness of our method. In the future, we plan to explore comprehensive strategies for modeling phonological information by integrating our PCWE with other resources [7] or methods [6], and exploit the core idea of the proposed method to address other state-of-the-art NLP tasks, including sentiment analysis [57], reader emotion classification [58], review interpretation [59], empathetic dialogue systems [60], end-to-end dialogue systems [61], and stock market prediction [62,63].…”

Section: Discussionmentioning

confidence: 99%

Pronunciation-Enhanced Chinese Word Embedding

Yang

Xie²,

Cheng

et al. 2021

Cogn Comput

Self Cite

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Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.

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“…EEG signals classification is also essential for diagnosing and treating brain diseases [84]. In addition, classification of emotion are widely concerned by scholars, not only within biomedical field, but also in social science research (e.g., [85][86][87][88]).…”

Section: Topical Correlationsmentioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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Sentiment strength detection with a context-dependent lexicon-based convolutional neural network

Cited by 46 publications

References 10 publications

COVID-19 Outbreak through Tweeters’ Words: Monitoring Italian Social Media Communication about COVID-19 with Text Mining and Word Embeddings

COVID-19 Outbreak through Tweeters’ Words: Monitoring Italian Social Media Communication about COVID-19 with Text Mining and Word Embeddings

Pronunciation-Enhanced Chinese Word Embedding

Topics and trends in artificial intelligence assisted human brain research

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