Sentiment analysis on film review in Gujarati language using machine learning

Shah, Poojan; Swaminarayan, Priya; Patel, Maitri

doi:10.11591/ijece.v12i1.pp1030-1039

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…Word n-grams: Several component evaluation approaches are available for AI-based, more aesthetic implementation, such as n-gram, tf-idf, count vectorizer, and word integration [57]. The n-grams feature combines n consecutive words or characters that are considered beneficial for categorizing texts.…”

Section: Methodsmentioning

confidence: 99%

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Maruf¹,

Ziyad²,

Haque³

et al. 2022

IJACSA

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In the human body, emotion plays a critical function. Emotion is the most significant subject in human-machine interaction. In economic contexts, emotion detection is equally essential. Emotion detection is crucial in making any decision. Several approaches were explored to determine emotion in text. People increasingly use social media to share their views, and researchers strive to decipher emotions from this medium. There has been some work on emotion detection from the text and sentiment analysis. Although some work has been done in which emotion has been recognized, there are many things to improve. There is not much work to detect racism and analysis sentiment on Ukraine -Russia war. We suggested a unique technique in which emotion is identified, and the sentiment is analyzed. We utilized Twitter data to analyze the sentiment of the Ukraine-Russia war. Our system performs better than prior work. The study increases the accuracy of detecting emotion. To identify emotion and racism, we used classical machine learning and the ensemble method. An unsupervised approach and NLP modules were used to analyze sentiment. The goal of the study is to detect emotion and racism and also analyze the sentiment.

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Section: Methodsmentioning

confidence: 99%

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Maruf¹,

Ziyad²,

Haque³

et al. 2022

IJACSA

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“…In other words, SVM will choose a hyperplane with the maximum margin, as shown in Figure 2(b). To select the optimal hyperplane, we use (7), (8), and (9) [35].…”

Section: Support Vector Machinementioning

confidence: 99%

“…In (7), 𝑊 is a normal vector, which determines the direction of the hyperplane, and b is a displacement, which determines the distance between the hyperplane and the origin. Assume that the hyperplane can correctly classify the training data.…”

Section: Support Vector Machinementioning

confidence: 99%

“…Based on this learning, machine learning can perform classification [4], [5]. Several machine learning models can be used to classify, i.e., Naive Bayes classifier [6], [7] and support vector machine (SVM) [8], [9]. Several studies have been performed to detect the level of banana ripeness [10] using the SVM method to classify banana ripeness levels.…”

Section: Introductionmentioning

confidence: 99%

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Classification of six banana ripeness levels based on statistical features on machine learning approach

Kosasih,

Sudaryanto,

Fahrurozi

2023

IJAAS

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<span>Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.</span>

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“…Bersamaan dengan tren tersebut, semakin banyak situs website di mana konsumen dapat menulis dan membaca ulasan, serta mengungkapkan pengalaman, peasant, pendapat, pandangan, dan keluhan mereka tentang berbagai produk dan layanan [1]. Sentimen dari pengguna menjadi hal penting dikarenakan jika ada yang ingin membeli produk baru, mendaftar kursus, atau ingin menonton film mereka akan melihat dan melakukan pengecekan terhadap review-nya terlebih dahulu dan berdasarkan review tersebut mereka membuat keputusan [2]. Salah satu aspek spesifik dari platform media sosial ialah sentiment analysis [3].…”

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Sentiment Analysis Komentar Berbahasa Bengkulu Menggunakan Long Short-Term Memory (LSTM)

Nurhaeni,

Faurina,

Utama

et al. 2023

pseudocode

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Salah satu jenis informasi yang bisa didapatkan dari media sosial adalah informasi sentimen. Sentimen memiliki arti pendapat dan pandangan masyarakat yang mengandung perasaan di dalamnya. Analisis sentimen dapat digunakan untuk menganalisis nilai sentimen apakah sentimen tersebut merupakan sentimen yang cenderung netral, negatif, atau positif. Pemodelan analisis sentimen untuk berbagai bahasa diperlukan karena setiap bahasa memiliki karakteristik dan keunikannya masing-masing, tidak terkecuali Bahasa Bengkulu. Pemodelan sentimen Bahasa Bengkulu belum tersedia, oleh karena itu pada penelitian ini akan dikembangkan model analisis sentimen Bahasa Bengkulu dengan menerapkan Long Short-Term Memory (LSTM) dan dilakukan eksperimen arsitektur Long Short-Term Memory (LSTM) untuk mendapatkan arsitektur yang menghasilkan nilai terbaik. Data yang digunakan dalam penelitian berjumlah 24.000 komentar berbahasa Bengkulu yang diperoleh dari media sosial Instagram, Twitter, dan Youtube. Penelitian eksperimen 1 menghasilkan nilai akurasi yang paling baik dibandingkan dengan hasil pengujian pada eksperimen lainnya, dengan nilai akurasi sebesar 0,87 dengan presicission bernilai 0,80, recall bernilai 0,82 dan F1-score bernilai 0,81.

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Sentiment analysis on film review in Gujarati language using machine learning

Cited by 14 publications

References 22 publications

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Classification of six banana ripeness levels based on statistical features on machine learning approach

Sentiment Analysis Komentar Berbahasa Bengkulu Menggunakan Long Short-Term Memory (LSTM)

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