Sentiment Analysis of US Airlines Tweets Using LSTM/RNN

Monika, R.; Deivalakshmi, S.; Janet, B.

doi:10.1109/iacc48062.2019.8971592

Cited by 42 publications

(16 citation statements)

References 16 publications

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“…Because of big data, machine learning algorithms can now find finer-grained trends and create more exact and timely forecasts than they have ever been able to do previously [ 24 ]. Deep learning algorithms are used to identify objects in photos [ 25 ], convert spoken words to text [ 26 ], match news articles and goods to user interests, and show relevant search results [ 27 ].…”

Section: Related Literaturementioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

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Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.

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Section: Related Literaturementioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

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“…The machine learning algorithm may be regarded as a computational model that handles input data to achieve a goal and deliver a specific outcome Machine learning is also used to create an algorithm that aids in the improvement of a system's performance output based on knowledge or experience [9].Similarly, deep neural network algorithms show meaningful improvements over existing machine learning approaches in various domains like speech recognition & computer vision [10].Sentiment classification techniques were used to classify US airline tweets based on sentiment polarity due to flight services as positive, negative and neutral connotations on six different US airlines. The word embedding models (Word2Vec, Glove) detect sentiment polarity using deep learning methods and LSTM is a powerful classifier for sentiment analysis [11]. Deep learning-based sentiment analysis financial news predicts the change in price earlier based on the polarity of data.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Aryal¹,

Bhattarai²

2021

Adv. Engin. & Technol.

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Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

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“…Besides, the study by Qian et al [16] revealed that LSTM behaves efficiently when used on different text levels of weather-and-mood tweets. After reviewing some recent studies [1,11,12,15,[17][18][19][20], we found that CNN and RNN are outperforming methods with a relatively high overall accuracy. Both shallow neural networks and deep neural networks are capable of approximating any function.…”

Section: Introductionmentioning

confidence: 94%

Hybrid Deep Learning Models for Sentiment Analysis

2021

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Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

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Sentiment Analysis of US Airlines Tweets Using LSTM/RNN

Cited by 42 publications

References 16 publications

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Hybrid Deep Learning Models for Sentiment Analysis

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