LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Chiny, Mohamed; Chihab, Marouane; Bencharef, Omar; Chihab, Younes

doi:10.14569/ijacsa.2021.0120730

Cited by 31 publications

(18 citation statements)

References 33 publications

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“…Yang et al [25] used the BERT model to train word vectors, used a sentiment dictionary to enhance the sentiment features in the text, and then went through a convolutional layer and a pooling layer to classify the weighted sentiment features. Chiny et al [26] proposed a hybrid sentiment analysis model based on Long Short-Term Memory network (LSTM), a rule based sentiment dictionary (VADER) and TF-IDF weighting method. The above three methods each get a sentiment score, and then treated the three scores as three inputs, ans used classification models such as Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM) for sentiment polarity classification.…”

Section: Sentiment Analysis Integrating Sentiment Dictionary and Mach...mentioning

confidence: 99%

“…TF or term frequency refers to the frequency of word in a text) and IDF refers to the inverse text frequency index. The essential idea underlying TF-IDF is that words that appear more frequently in one document and less in others should be more important because they are more useful for classification [26]. Therefore, It is widely used in keyword extraction, text similarity comparison and topic classification.…”

Section: B Tf-idfmentioning

confidence: 99%

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A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

Hao

Xiao

2022

IEEE Access

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The most commonly used methods in text sentiment analysis are rule-based sentiment dictionary and machine learning, with the later referring to the use of vectors to represent text followed by the use of machine learning to classify the vectors. Both methods have their limitations, including inflexibility of rules, non-prominence of sentiment words. In this paper, we design a weight distributing method combining the two methods for text sentiment analysis, by which the sentence vectors obtained can both highlight words with sentiment meanings while retaining their text information. Empirical results show that based on this new method, the accuracy rate of text sentiment analysis can reach as high as 82.1%, which means 13.9% higher than rule-based sentiment dictionary method, and 7.7% higher than TF-IDF weighting method.

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Section: Sentiment Analysis Integrating Sentiment Dictionary and Mach...mentioning

confidence: 99%

Section: B Tf-idfmentioning

confidence: 99%

A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

Hao

Xiao

2022

IEEE Access

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“…The Fruquency Inverse document Frequency (TF IDF) algorithm is often applied to text for sentiment analysis because it can be used to evaluate the importance of words in a corpus. The more often the word appears, the more useful it will be for the classification process (Chiny et al, 2021). Here's the formula in TF-IDF weighting (Liu & Yang , 2012).…”

Section: Tf-idf Weightingmentioning

confidence: 99%

Sentiment Analysis of Three-Period Polemics Using K-Nearest Neighbor With Tf-Idf Weighting

Ernawati

Wati

2022

J. Ris. Inform.

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The issue of changing the presidential term which was originally 2 periods of government into 3 periods raises pros and cons in the community. Many 3-period hashtags have sprung up on social media twitter. So that conducted research on sentiment analysis of presidential election polemics 3 period. The purpose of the study was to produce the value of classification on the issue of presidential election change discourse into 3 periods using the K-NN method and whether the k-NN method proved to be well used for classifying text in the review of presidential election polemics 3 periods. Dataset totaling 1152 data, data is processed using Python and Jupyter Notebook as a text editor. The data is classified into positive reviews and negative reviews, then the data is divided into training data and test data with a ratio of 90:10. Weighting words using TF-IDF and sentiment classification using K-NN method. From the results of classification using the K-NN method obtained the highest accuracy when the value of k=17 and k = 18 with an accuracy of 85.3%. The results of the analysis of public sentiment to review the issue of discourse on the change of presidential term into 3 periods tend to be negative with a percentage of 21.26% positive sentiment and 78.74% negative sentiment.

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“…A sentiment lexicon, or set of lexical characteristics commonly categorized as positive or negative depending on their semantic orientation, is used by VADER. In order to obtain a reliable point estimate of the sentiment valence (intensity) of each lexical attribute, VADER is based on a wisdom of crowds (WotC) technique [9]. With English-language sentiment lexicons, VADER uses gold-standard algorithms.…”

Section: Introductionmentioning

confidence: 99%

Opinions for Receiving COVID-19 Vaccines Based on Sentiment Analysis

2022

pnr

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Due to vaccination hesitancy/refusal, a call to action is urgently needed for the COVID-19 vaccine deployment. Find out what the public thinks about the COVID-19 vaccine. Sentiment analysis is a technique for getting text sentiment scores. Therefore, we proposed architecture to analyze the textual data collection of people's opinions on COVID-19 vaccines using two of the best sentiment analysis techniques, the Bidirectional Encoder Representations from Transformers (BERT) technique and the Valence Aware Dictionary for sEntiment Reasoning (VADER) technique of Natural Language Processing (NLP). A questionnaire survey of corona vaccines recipients who recommend COVID-19 collected the data. Finally, recommendations for the corona vaccine were investigated, and various studies were done to determine its efficacy. Accuracy, precision, recall, and f1-score are standard evaluation criteria. The data shows the proposed model's excellent sentiment analysis performance, indicating that most vaccine users prefer to recommend others to get the vaccines.

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LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Cited by 31 publications

References 33 publications

A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

Sentiment Analysis of Three-Period Polemics Using K-Nearest Neighbor With Tf-Idf Weighting

Opinions for Receiving COVID-19 Vaccines Based on Sentiment Analysis

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