Sentiment classification with GST tweet data on LSTM based on polarity-popularity model

Das, Sourav; Das, Dipankar; Kolya, Anup Kumar

doi:10.1007/s12046-020-01372-8

Cited by 13 publications

(15 citation statements)

References 44 publications

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“…• This model is not suitable for real time applications. [4] L S T M • It attains an accuracy rate of 84.51% and also increases the testing accuracy.…”

Section: Methodology Featuresmentioning

confidence: 93%

“…In [4] had gathered 2000 k Tweeter commands from Goods and Services Tax (GST) from June 2017 to December 2017. This model has collected a topic-sentiment system using LSTM by considering the tweets regarding GST.…”

Section: Related Workmentioning

confidence: 99%

“…Sentiment analysis [3] is the procedure of "analyzing the meaning of the text and the sentiment hidden in the text by using natural language processing technology" for disassembling, modeling, extracting, reasoning, and classifying the text [18]. The major aim of sentiment analysis [4] is to discover the attitude of bipolar prospects on specific targets in a sentence [41]. In recent years, diverse approaches have been proposed for sentiment analysis by considering the complex text polarity [44].…”

Section: Introductionmentioning

confidence: 99%

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SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Kayıkçı

2022

Multimed Tools Appl

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Sentiment analysis is one of the efficient models for extracting opinion mining with identification and classification from unstructured text data such as product reviews or microblogs. It is used to gain feedback from political campaigns, brand reviews, marketing analysis, and customers. The sentiment analysis on Twitter data is a recent research field in the natural processing. The dataset is gathered from the "Twitter" package in R along with Twitter API. The main intent of this paper is to understand the public opinion on the recently implemented demonetization policy using the proposed SenDemonNet. Initially, the tweet preprocessing was done, which is intended for cleaning the text data. Then, the feature extraction is performed by Bag of n-grams, TF-IDF, and the word2vec algorithm. The main objective of this work is a weighted feature selection that is developed by the hybrid Forest-Whale Optimization Algorithm (F-WOA) to get the best classification outcome. With these features, the Heuristic Deep Neural Network (HDNN) is adopted for classification, where the proposed FOA and WOA tune the parameter of DNN for reaching the maximum accuracy rate. From the statistical analysis, the performance of the designed F-WOA-DNN is 1.8%, 1.9%, 1.86%, and 2% enhanced than PSO-DNN, GWO-DNN, WOA-DNN, FOA-DNN, SVM, CNN, LSTM, and DNN respectively. Extensive experimental results show that SenDemonNet outperforms its competitors, producing an impressive increase in the classification accuracy on the benchmark dataset.

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“…• This model is not suitable for real time applications. [4] L S T M • It attains an accuracy rate of 84.51% and also increases the testing accuracy.…”

Section: Methodology Featuresmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Kayıkçı

2022

Multimed Tools Appl

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“…Since then, it has been a focal point of research. Sentiment analysis of such large-scale events has been under the focus for a decade now [4][5][6]. Most of such works, and overall sentiment classification has also experimented on other similar social media users' opinions.…”

Section: Introductionmentioning

confidence: 99%

“…5 Recently we have also witnessed the shifting of the coronavirus epicenter from China to Europe, resulting in a major number of deaths in Italy and Spain. 6 It is evident without saying that an incident of such a large statue has already attracted a plethora of research works relating the Twitter-based sentiment analysis from a healthcare standpoint. Real-world applications of such sentiment evaluation outcomes can benefit us in multiple ways and domains.…”

Section: Introductionmentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

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Sentiment Analysis of Covid-19 Tweets Using Evolutionary Classification-Based LSTM Model

Chakraborty¹,

Das

Kolya³

2021

Advances in Intelligent Systems and Computing

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As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lockdown to check its intensity. During this lockdown period, social media platforms have played an important role to spread information about this pandemic across the world, as people used to express their feelings through the social networks. Considering this catastrophic situation, we developed an experimental approach to analyze the reactions of people on Twitter taking into account the popular words either directly or indirectly based on this pandemic. This paper represents the sentiment analysis on collected large number of tweets on Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on the topics related to Covid-19 epidemic using an evolutionary classification followed by the n-gram analysis. Then we calculated the sentiment ratings on collected tweet based on their class. Finally, we trained the long-short term network using two types of rated tweets to predict sentiment on Covid-19 data and obtained an overall accuracy of 84.46%.

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Sentiment classification with GST tweet data on LSTM based on polarity-popularity model

Cited by 13 publications

References 44 publications

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Sentiment Analysis of Covid-19 Tweets Using Evolutionary Classification-Based LSTM Model

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