Text based Sentiment Analysis using LSTM

Murthy, G. S. N.; Allu, Shanmukha Rao; Andhavarapu, Bhargavi; Bagadi, Mounika Belusonti Mounika

doi:10.17577/ijertv9is050290

Cited by 54 publications

(24 citation statements)

References 4 publications

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“…Dalam analisis sentimen, metode LSTM telah banyak digunakan karena memberikan hasil yang lebih bagus daripada algoritma machine learning. Salah satu contoh penelitian analisis sentimen yang telah dilakukan adalah Murthy et al (2020) pada data IMDB dan Amazon product menggunakan algoritma Long Short-Term Memory (LSTM) mengenai ulasan film dan produk. Data yang digunakan sebanyak 50.000 ulasan, 25.000 di antaranya terpolarisasi positif dan 25.000 terpolarisasi negatif.…”

Section: Pendahuluanunclassified

Analisis Sentimen Vaksin Covid-19 Pada Twitter Menggunakan Recurrent Neural Network (Rnn) Dengan Algoritma Long Short-Term Memory (Lstm)

Maharani,

Warsito,

Santoso

2024

J.Gauss

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The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.

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Section: Pendahuluanunclassified

Analisis Sentimen Vaksin Covid-19 Pada Twitter Menggunakan Recurrent Neural Network (Rnn) Dengan Algoritma Long Short-Term Memory (Lstm)

Maharani,

Warsito,

Santoso

2024

J.Gauss

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“…A type of recurrent neural network that is capable of capturing long-term dependencies in input data. [60,61] Artificial Neural Networks (ANNs)…”

Section: Technique Description Reference(s)mentioning

confidence: 99%

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Anwar,

Rehman,

Nasralla

et al. 2023

Education Sciences

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In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.

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“…It is known that deep convolutional networks and recurrent networks are excellent models, but have different strengths and weaknesses. Behera et al proposed CO-LSTM models, which have great compatibility with different fields and adaptability for processing mass social data [18]. Others are working on refining the way sentiments are extracted.…”

Section: Sentiment Analysismentioning

confidence: 99%

Bitcoin price prediction based on sentiment analysis and LSTM

Wen,

Wu,

Shen

et al. 2023

ACE

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As cryptocurrencies become widely accepted due to technical improvements, reliable approaches to capture their future price movements of them become critical. This study mainly combines weighted sentiment analysis results from social media-related comments and financial news headlines with a stacked LSTM model to predict second-day Bitcoin price evolution. This study also compared our results and the results produced by MLP, RF, and SVM after feeding the sentiment analysis results.

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Text based Sentiment Analysis using LSTM

Cited by 54 publications

References 4 publications

Analisis Sentimen Vaksin Covid-19 Pada Twitter Menggunakan Recurrent Neural Network (Rnn) Dengan Algoritma Long Short-Term Memory (Lstm)

Analisis Sentimen Vaksin Covid-19 Pada Twitter Menggunakan Recurrent Neural Network (Rnn) Dengan Algoritma Long Short-Term Memory (Lstm)

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Bitcoin price prediction based on sentiment analysis and LSTM

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