Sentiment analysis using global vector and long short-term memory

Kusum, Kusum; Panda, Supriya P.

doi:10.11591/ijeecs.v26.i1.pp414-422

Cited by 5 publications

(4 citation statements)

References 26 publications

(31 reference statements)

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“…Next process is data analysis that started with word vectorization (transforming the word to numerical presentation) [26], and identifying word polarity using TextBlob [17]. TextBlob is a lexicon-based text analyzer which applies natural language toolkit (NLTK) to process the text [17]. Word polarity is used in identifying the score of the word to either positive, negative, or neutral.…”

Section: Research Phasesmentioning

confidence: 99%

“…Sentiment analysis faces challenges such as categorizing text in the form of sarcasm, irony, context-dependent sentiment, and handling mixed sentiments within a single text. Businesses use it to analyze customer feedback, reviews, and social media comments to gauge customer satisfaction [16], [17]. The problem arises from the limited focus on popular social media platforms, such as Twitter, for sentiment analysis and data mining, which might not provide a holistic representation of the diverse approaches and discussions within the data science community.…”

Section: Introductionmentioning

confidence: 99%

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Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Mohamad,

Masrom,

Salleh

et al. 2024

IJEECS

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Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.

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Section: Research Phasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Mohamad,

Masrom,

Salleh

et al. 2024

IJEECS

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“…The LSTM model is a development of the recurrent neural network (RNN) model to overcome vanishing gradient or exploding gradient problems [16]. With the existence of 3 gates, which include the input gate, the forget gate and the output gate, it can function to control the flow of information in and out of the memory cell [17].…”

Section: Model Buildingmentioning

confidence: 99%

Determining community happiness index with transformers and attention-based deep learning

Wicaksana,

Kusumaningrum,

Gernowo

2024

IJ-AI

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In the current digital era, evaluating the quality of people's lives and their happiness index is closely related to their expressions and opinions on Twitter social media. Measuring population welfare goes beyond monetary aspects, focusing more on subjective well-being, and sentiment analysis helps evaluate people's perceptions of happiness aspects. Aspect-based sentiment analysis (ABSA) effectively identifies sentiments on predetermined aspects. The previous study has used Word-to-Vector (Word2Vec) and long short-term memory (LSTM) methods with or without attention mechanism (AM) to solve ABSA cases. However, the problem with the previous study is that Word2Vec has the disadvantage of being unable to handle the context of words in a sentence. Therefore, this study will address the problem with bidirectional encoder representations from transformers (BERT), which has the advantage of performing bidirectional training. Bayesian optimization as a hyperparameter tuning technique is used to find the best combination of parameters during the training process. Here we show that BERT-LSTM-AM outperforms the Word2Vec-LSTM-AM model in predicting aspect and sentiment. Furthermore, we found that BERT is the best state-of-the-art embedding technique for representing words in a sentence. Our results demonstrate how BERT as an embedding technique can significantly improve the model performance over Word2Vec.

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“…The global vector (GloVe) on the otherhand incorporates a global matrix factorization and local context window techniques through a bilinear regression model. It builds a corpus matrix by converting words into vectors [17]. The FastText model handles the Out of Vocabulary problem by working at character-n-gram level while the Embeddings from Language Models(ELMo) provides each term with a representation derived from the entire collection of sentences to which it belongs [18].…”

Section: What Are Embeddings and How Are They Generated?mentioning

confidence: 99%

Optimizing Sales Prediction in Online Grocery Shopping using Product Embeddings

Aziegbemhin

2023

Preprint

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The focus of this research is to delve into the world of product embeddings and their potential to improve prediction accuracy. The study included a thorough review of existing literature to highlight the generation of embeddings and their utility in downstream tasks. This review uncovered the significance of embeddings in enhancing prediction accuracy, showing promise for their application in the realm of online grocery shopping. By employing embedding techniques, we aim to examine how they refine sales predictions, ultimately contributing to improved inventory management and optimized supply chain processes in the e-commerce domain. Future work will encompass data acquisition and a comprehensive process involving embedding generation, model training, and hyperparameter tuning to validate these findings.

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Sentiment analysis using global vector and long short-term memory

Cited by 5 publications

References 26 publications

Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Determining community happiness index with transformers and attention-based deep learning

Optimizing Sales Prediction in Online Grocery Shopping using Product Embeddings

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