Sentiment Analysis on Textual Reviews

Karim, Mirsa; Das, Smija

doi:10.1088/1757-899x/396/1/012020

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…Compound score adalah jumlah keseluruhan skor positif, negatif dan netral yang telah dinormalisasi antara -1 dan +1. Jika compound score >= 0.05 maka sentiment positif, jika nilai compound score > -0.05 dan nilai compound score < 0.05 maka sentimen netral, jika nilai compound score <= -0.05 maka sentimen negative [19].…”

Section: Pertumbuhanunclassified

Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik

Afrianto¹,

Fudholi

Rani³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Stock market is one economic driver. It has roles in growth and development of a country. Stock is an attractive investment due to the huge profit. Many people buy and sell their stock. Stock investors try to choose the good investment company to get profits with small risk. Therefore, stock investors need to be careful and must evaluate a company. With machine learning technology, stock prediction problems can be solved. Deep learning is a subset of machine learning with own network. Deep learning has good performance in managing large amounts of data. This study used stock price history data and public sentiment data on a company. The method used in this research is Bidirectional Long-Short Term Memory (BiLSTM). The features used were closing price and compound score value of the public sentiment. Four scenarios were used in finding the best predictive model. The four scenarios use the same test data with different lengths of training data window. From the modelling, predictions with the model built using BiLSTM resulted in the smallest MSE value of 0.094 and the smallest RMSE value of 0.306.

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Section: Pertumbuhanunclassified

Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik

Afrianto¹,

Fudholi

Rani³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…This model takes movie reviews dataset from cornell university which are labeled with positive and negative labels. This model uses rule based mechanism that uses mining algorithms to find the features and opinion for a particular product [14].…”

Section: Related Workmentioning

confidence: 99%

“…Mirsa Karim and Smija Das model, works on Valence Aware Dictionary and Sentiment Reasoner (VADER) that takes the compound score. Based on compound score is more then and equal to 0.5 consider as positive, less then and equal to -0.5 consider as negative and -0.5 to 0.5 score consider as neutral [14].…”

Section: Related Workmentioning

confidence: 99%

“…This model worked on LDA analysis for NAÏVE BAYES, sentiment VADER and sentiword net techniques for finding the compound score, POS tagging, synset score to build vocabulary and training. The main aim of this model to find more accurate and precise anticipating the conclusion of sentence as well as found sentiment associate with sentence [14].…”

Section: Related Workmentioning

confidence: 99%

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Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

Sonare¹,

Kamble²

2021

IJAINN

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Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.

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“…Sosial Media Menggunakan Orange Data Mining tetapi dalam hal ini bisa menggunakan sebuah kamus dengan antonim dan sinonim dari kata-kata dan frase opini mereka dengan orientasi sentimen masingmasing [1] dengan metode Rule-based Sentiment Analysis metode ini secara efektif menggunakan algoritma rule mining untuk menemukan fitur suatu produk dan untuk mengetahui pendapat yang terkait dengan produk tertentu [2]. Untuk melakukan analisis sentimen berbasis rule-based sentiment analysis dapat mengikuti kerangka kerja berikut (Kundi dkk., 2014) di sesuaikan dengan alur kerja penelitian ini:…”

Section: Perbandingan Analisis Sentimen Terhadap Digital Payment "Tunclassified

Comparison of Sentiment Analysis against Digital Payment “T-cash and Go-pay” in Social Media Using Orange Data Mining

Anggraini

Suroyo

2019

journalisi

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AbstrakSaat ini pembicaraan publik di sosial media menjadi salah satu hal menarik untuk diteliti. Dari topik pembicaraan itu menghasilkan komentar yang sebagian besar mengandung opini sentimen. Penelitian ini mencoba menganalisis komentar dengan metode analisis vader, yaitu metode analisis lexicon-based berbasis rule-based sentiment analysis. Vader akan menganalisis text berdasarkan lexicon (a library) yang menghasilkan class sentimen berupa positif, negatif, dan neutral dengan tambahan skor total atau compound (combined score). Penelitian ini memanfaatkan Prepocess text yang meliputi transformation, tokenization, normalization, dan filtering yang bertujuan agar text bisa dianalisis oleh Orange Data Mining guna mendapat perbandingan analisis sentimen terhadap T-cash dan Go-pay di sosial media. Dari penelitian yang dilakukan mendapat kesimpulan bahwa T-cash memiliki nilai sentimen positif lebih tinggi dari pada Go-pay dan memiliki sentimen negatif yang lebih rendah dari pada Go-pay. Namun persamaanya T-cash dan Go-pay memiliki kesamaan pola grafik dimana sentimen terbesar adalah neutral, diikuti oleh positif, dan terakhir adalah negative.

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Sentiment Analysis on Textual Reviews

Cited by 15 publications

References 9 publications

Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik

Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik

Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

Comparison of Sentiment Analysis against Digital Payment “T-cash and Go-pay” in Social Media Using Orange Data Mining

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