Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis

Alshari, Eissa M.; Azman, Azreen; Doraisamy, Shyamala; Mustapha, Norwati; Alkeshr, Mostafa

doi:10.1109/infrkm.2018.8464775

Cited by 41 publications

(16 citation statements)

References 8 publications

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“…Experiment results using lexicon-based data dictionary in Arabic language have been obtained so far better [12]. In Alshari et al [13] authors described SentiWordNet (SW) as a curse of dimensionality, they used sentimental lexicon dictionary based on word2vec to perform SA. Besides, in Bangla text, author [14] preprocessed data to carry through a SA by taking TF-IDF vectorizer and classified the data with support vector machine (SVM) algorithm, however they did not measure the polarity by calculating the score of a text; hence it is required to detect the polarity of each sentence by a specific rule-based [15] algorithm.…”

Section: Related Workmentioning

confidence: 99%

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Bhowmik¹,

Arifuzzaman²,

Mondal³

et al. 2021

NLPRE

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With the proliferation of the Internet's social digital content, sentiment analysis (SA) has gained a wide research interest in natural language processing (NLP). A few significant research has been done in Bangla language domain because of having intricate grammatical structure on text. This paper focuses on SA in the context of Bangla language. Firstly, a specific domain-based categorical weighted lexicon data dictionary (LDD) is developed for analyzing sentiments in Bangla. This LDD is developed by applying the concepts of normalization, tokenization, and stemming to two Bangla datasets available in GitHub repository. Secondly, a novel rule-based algorithm termed as Bangla Text Sentiment Score (BTSC) is developed for detecting sentence polarity. This algorithm considers parts of speech tagger words and special characters to generate a score of a word and thus that of a sentence and a blog. The BTSC algorithm along with the LDD is applied to extract sentiments by generating scores of the two Bangla datasets. Thirdly, two feature matrices are developed by applying term frequency-inverse document frequency (tf-idf) to the two datasets, and by using the corresponding BTSC scores. Next, supervised machine learning classifiers are applied to the feature matrices. Results show that for the case of BiGram feature, support vector machine (SVM) achieves the best classification accuracy of 82.21% indicating the effectiveness of BTSC algorithm in Bangla SA.

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Section: Related Workmentioning

confidence: 99%

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Bhowmik¹,

Arifuzzaman²,

Mondal³

et al. 2021

NLPRE

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“…With the development of text learning research, text representation methods that incorporate contextual semantics, such as GloVe, word2vec, and Bert [7][8][9][10][11] , have begun to be applied to text sentimental analysis.…”

Section: Sentiment Analysis Based On Textmentioning

confidence: 99%

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

Sun¹,

Gao²

2021

Electron Sci Technol Appl

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<p>In recent years, more and more people express their feelings through both images and texts, boosting the growth of multimodal data. Multimodal data contains richer semantics and is more conducive to judging the real emotions of people. To fully learn the features of every single modality and integrate modal information, this paper proposes a fine-grained multimodal sentiment analysis method FCLAG based on gating and attention mechanism. First, the method is carried out from the character level and the word level in the text aspect. CNN is used to extract more fine-grained emotional information from characters, and the attention mechanism is used to improve the expressiveness of the keywords. In terms of images, a gating mechanism is added to control the flow of image information between networks. The images and text vectors represent the original data collectively. Then the bidirectional LSTM is used to complete further learning, which enhances the information interaction capability between the modalities. Finally, put the multimodal feature expression into the classifier. This method is verified on a self-built image and text dataset. The experimental results show that compared with other sentiment classification models, this method has greater improvement in accuracy and F1 score and it can effectively improve the performance of multimodal sentiment analysis.</p>

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“…Word2vec takes text input and produces a large vector space. Word Embedding based approaches are used widely in recent research studies in sentiment analysis [38][39][40].…”

Section: ) Word2vecmentioning

confidence: 99%

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

et al. 2020

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In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

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Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis

Cited by 41 publications

References 8 publications

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

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