Optimizing Word Embedding for Fine-Grained Sentiment Analysis

Zhang, Wei; Zhang, Yue; Yang, Kehua

doi:10.1007/978-3-030-24265-7_24

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Word embedding techniques learn the relation between words via training on context examples of each word (Ganguly, 2020 ) using deep learning methods. Some of the most commonly used word embeddings are GloVe (Gomez et al, 2018 ), Word2vec (Jiang et al, 2020 ), and WordRank (Zhang, 2019 ). Pre-trained word embeddings in a variety of languages are available online, something that boosted their application on an impressive number of different fields.…”

Section: Word Embeddings and Instagram Hashtagsmentioning

confidence: 99%

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

2023

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Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.

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Section: Word Embeddings and Instagram Hashtagsmentioning

confidence: 99%

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

2023

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A Comprehensive Study of Sentiment Analysis in Big Data Applications

Naik

Anasari

Gunjan

et al. 2020

Lecture Notes in Electrical Engineering

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Long text feature extraction network with data augmentation

Tang

Cui

et al. 2022

Appl Intell

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The spread of COVID-19 has had a serious impact on either work or the lives of people. With the decrease in physical social contacts and the rise of anxiety on the pandemic, social media has become the primary approach for people to access information related to COVID-19. Social media is rife with rumors and fake news, causing great damage to the Society. Facing shortages, imbalance, and nosiness, the current Chinese data set related to the epidemic has not helped the detection of fake news. Besides, the accuracy of classification was also affected by the easy loss of edge characteristics in long text data. In this paper, long text feature extraction network with data augmentation (LTFE) was proposed, which improves the learning performance of the classifier by optimizing the data feature structure. In the stage of encoding, Twice-Masked Language Modeling for Fine-tuning (TMLM-F) and Data Alignment that Preserves Edge Characteristics (DA-PEC) was proposed to extract the classification features of the Chinese Dataset. Between the TMLM-F and DA-PEC processes, we use Attention to capture the dependencies between words and generate corresponding vector representations. The experimental results illustrate that this method is effective for the detection of Chinese fake news pertinent to the pandemic.

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Optimizing Word Embedding for Fine-Grained Sentiment Analysis

Cited by 3 publications

References 12 publications

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

A Comprehensive Study of Sentiment Analysis in Big Data Applications

Long text feature extraction network with data augmentation

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