Sentimental analysis using recurrent neural network

Thomas, Merin; C.A, Latha

doi:10.14419/ijet.v7i2.27.12635

Cited by 18 publications

(5 citation statements)

References 10 publications

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“…Recurrent Neural Networks (RNN) are capable of keeping a record of all input values that have been seen by the network, including the current input. The hidden values in each layer of the RNN network depend on all the inputs that have been previously processed in a particular order (Thomas & Latha, 2018). In this model, every sentence was initially introduced to the recursive neural network as a concealed layer, and subsequently, the associated vectors were treated as inputs to the recurrent neural network.…”

Section: Rnnmentioning

confidence: 99%

Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments

Putra,

Sibaroni

2024

SinkrOn

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In today's development, the process of buying and selling transactions between sellers and buyers is so developed. not only done directly but can also be done online or can be called e-commerce. Which is where the development of technology is so fast that it indirectly encourages entrepreneurs to develop through e-commerce. Lazada is one of the online stores in Indonesia that has many users and Lazada makes it easy to shop without the need to come to the place or directly. However, purchasing goods using e-commerce has problems regarding the quality of the goods you want to buy, therefore purchasing goods can be seen through reviews of each one you want to buy. Sentiment analysis is carried out using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). And using the Epoch value as a parameter in processing validation data and test data to produce the best accuracy value

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Section: Rnnmentioning

confidence: 99%

Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments

Putra,

Sibaroni

2024

SinkrOn

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“…Nowadays, opinion-rich tools such as evaluation sites, blogs, etc. are increasingly accessible and popular [25]. The feedback can even be part of a service or products sold in online stores like Amazon.…”

Section: A Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

2021

IJATCSE

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More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

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“…Opinion mining adalah bagian dari pekerjaan yang melakukan peninjauan berkaitan dengan perlakuan komputasional opini, sentimen, dan persepsi dari teks. Hal ini memungkinkan pengguna untuk mencari informasi tentang deteksi flame (bahasa yang sangat kasar) dalam email atau jenis komunikasi lainnya, persepsi produk baru, persepsi merk, dan manajemen reputasi [8]. Sentiment analysis pada penelitian ini menggunakan model Word2Vec sebagai fitur ekstraksi dan dipadukan dengan metode Recurrent Neural Network (RNN) Learning.…”

Section: Review Literatureunclassified

Sentiment Analysis Protokol Kesehatan Virus Corona Dari Tweet Menggunakan Word2vec Model Dan Recurrent Neural Network Learning

Anesca,

Muludi,

Shofiana

2021

Pepadun

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Sentiment analysis is a computational study of opinion from various opinions, which is part of the work that conducts a review related to the computational treatment of opinions, sentiments, and perceptions of the text. To solve various problems in sentiment analysis, needed a good text representation method. In this study, a deep learning analysis was carried out using the Recurrent Neural Network (RNN) method and the Word2Vec Model as word embedding in sentiment classification. The sentiment dataset used comes from user reviews on Twitter (tweets) on the health protocols implemented by the public from the government's appeal. The results showed that the RNN model using sigmoid activation resulted in the greatest accuracy of 66%. The training process in this test uses 10 epochs and 32 batch sizes so that the precision value for negative sentiment is 54% and for positive sentiment is 67%.

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Sentimental analysis using recurrent neural network

Cited by 18 publications

References 10 publications

Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments

Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

Sentiment Analysis Protokol Kesehatan Virus Corona Dari Tweet Menggunakan Word2vec Model Dan Recurrent Neural Network Learning

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