Sentiment aware word embeddings using refinement and senti-contextualized learning approach

Naderalvojoud, Behzad; Sezer, Ebru Akçapınar

doi:10.1016/j.neucom.2020.03.094

Cited by 29 publications

(18 citation statements)

References 19 publications

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“…The above two methods make Word2Vec suitable for large-scale corpus training. Naderalvojoud et al proposed an improved word embedding method, which added emotional information encoding to the word vector pre-trained by Word2Vec, making the word vector suitable for emotional text analysis [19]. Onan combined word embedding methods with cluster analysis, and it was proved that the text analysis accuracy was improved by the method [20].…”

Section: Word Vector Generationmentioning

confidence: 99%

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Yan

et al. 2021

Applied Sciences

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In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model.

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Section: Word Vector Generationmentioning

confidence: 99%

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Yan

et al. 2021

Applied Sciences

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“…Word embedding is a technique for analyzing the context of words in a sentence and converting them into a vector value. Word embedding methods include GloVe [16], Fasttext [17], and Word2vec [18]. Word2vec fails to consider the cooccurrence frequency of entire sentences because learning is performed only in a user-specified window.…”

Section: B Word2vec-based Word Embeddingmentioning

confidence: 99%

“…For example, 'kill' à (ki, il, ll). This generates a vector of words that are not found in the dictionary; moreover, its training process is fast [17]. Word2vec was proposed by Google after the neural network language model was improved [18].…”

Section: B Word2vec-based Word Embeddingmentioning

confidence: 99%

“…Thereby, accurate classification can be achieved; however, the proposed method was evaluated only on sentiment datasets. B. Naderalvojoud [25] developed a sentiment-recognition-based word-embedding deep learning model. It considers both the meaning and polarity of words and overcomes the weakness that words with different sentiments, but similar contexts may have similar vector values.…”

Section: B Word2vec-based Word Embeddingmentioning

confidence: 99%

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Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

2020

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With the increase in traffic accident rates, traffic risk detection is becoming increasingly important. Moreover, it is necessary to provide appropriate traffic information considering user locations and routes and design an analysis method accordingly. This paper proposes a word-embedding-based traffic document classification model for detecting emerging risks using a quantity termed sentiment similarity weight (SSW). The proposed method detects emerging risks by considering and classifying the importance and polarity of keywords in traffic document. Conventional sentiment analysis methods fail to utilize semantically significant keywords unless they are included in a sentiment dictionary. In this study, through word imputation using an established similarity dictionary and by widening the limited utilization range, the proposed method overcomes the disadvantage of sentiment dictionaries. The proposed method is evaluated through three tests. In the first, the similarity between keywords is measured, and thus model accuracy is evaluated. In the second test, three classifiers for emerging risk classification are compared. In the last test, emerging risk detection is assessed according to whether the proposed SSW is applied, and its effectiveness is therefore verified. The evaluation results demonstrate that the proposed traffic-related document classification model using the SSW has an f-measure of 0.907, indicating satisfactory performance. Therefore, the proposed SSW can be effectively used as a parameter in traffic-related document classification and enables the detection of emerging risks.

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“…In some NLP tasks like information retrieval [21,22], this situation may not be a problem. However, the performance of some NLP tasks such as sentiment analysis will be affected by this fact, because in sentiment analysis we do not rely only on contextual information to determine the sentence sentiment [23,24]. Since word embedding models ignore sentiment information, the closest words in the vector space may have opposite sentiment polarities.…”

Section: Introductionmentioning

confidence: 99%

Refining Word Embeddings with Sentiment Information for Sentiment Analysis

Kasri

Birjali²,

Mohamed³

et al. 2022

JICTS

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Natural Language Processing problems generally require the use of pre-trained distributed word representations to be solved with deep learning models. However, distributed representations usually rely on contextual information which prevents them from learning all the important word characteristics. The task of sentiment analysis suffers from such a problem because sentiment information is ignored during the process of learning word embeddings. The performance of sentiment analysis can be affected since two words with similar vectors may have opposite sentiment orientations. The present paper introduces a novel model called Continuous Sentiment Contextualized Vectors (CSCV) to address this problem. The proposed model can learn word sentiment embedding using its surrounding context words. It uses Continuous Bag-of-Words (CBOW) model to deal with the context and sentiment lexicons to identify sentiment. Existing pre-trained vectors are combined then with the obtained sentiment vectors using Principal component analysis (PCA) to enhance their quality. The experiments show that: (1) CSCV vectors can be used to enhance any pre-trained word vectors; (2) The result vectors strongly alleviate the problem of similar words with opposite polarities; (3) The performance of sentiment classification is improved by applying this approach.

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Sentiment aware word embeddings using refinement and senti-contextualized learning approach

Cited by 29 publications

References 19 publications

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

Refining Word Embeddings with Sentiment Information for Sentiment Analysis

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