Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

Lutfi, Anang Anggono; Permanasari, Adhistya Erna; Fauziati, Silmi

doi:10.20473/jisebi.4.1.57-64

Cited by 29 publications

(15 citation statements)

References 9 publications

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“…The Hierarchical Category Sentiment Analysis (HCSA) algorithm can summarize the main difficulties or points that need to be addressed when processing them into three parts. First, because the structure of sentiment categories in the Hierarchical Category Sentiment Analysis task is designed to be different from the mainstream sentiment analysis methods that are tiled, i.e., all sentiment categories are treated equally, but instead, different shallow sentiment categories are deeply layered and dispersed ( Lutfi et al, 2018 ). Similarly, in the identification of potential customers, we also pay more attention to the identification accuracy of minority potential customers.…”

Section: User Identification Algorithm Design For Sentiment Analysismentioning

confidence: 99%

Brand Potential User Identification Algorithm Based on Sentiment Analysis

2022

Front. Psychol.

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This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value.

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Section: User Identification Algorithm Design For Sentiment Analysismentioning

confidence: 99%

Brand Potential User Identification Algorithm Based on Sentiment Analysis

2022

Front. Psychol.

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“…Sentiment Analysis research in Indonesian is mostly based on classical machine learning methods, such as Naive Bayes (NB) [39], Support Vector Machine (SVM) [40], Decision Tree [41], and Maximum Entropy (ME) [42] which is based on a bag-of-words model that does not concern the order of a word in a sentence. On the other hand, deep learning algorithms take into account the order of words in a sentence.…”

Section: Text-based Sentiment Analysismentioning

confidence: 99%

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Setiawan¹

2021

IJIES

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Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three views into a fusion with an ensemble approach by a metaclassifier. We performed text classification with Deep Convolutional Neural Networks. The input feature is Word2Vec for text representation in order to preserve semantic meaning. Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model. We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron. Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning. A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features. The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.

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“…Penelitian tentang sentimen analisis pada ulasan produk sudah pernah dilakukan oleh peneliti sebelumnya. Penelitian oleh Lutfi dan Permatasari [11] menganalisis ulasan produk di marketplace Bukalapak untuk mengetahui sentimen ulasan pengguna bernilai positif atau negatif dengan pendekatan memanfaatkan Support Vector Machine dengan akurasi 93,42%. Pada penelitian yang dilakukan Muljono dan Dian [12], mempresentasikan analisis sentimen terhadap data opini di Twitter pada pelayanan situs marketplace di Indonesia menggunakan algoritma Naive Bayes dengan hasil akurasi sebesar 93.33%.…”

Section: Analisis Sentimen Pada Ulasan Pembelian Produk DI Marketplacunclassified

Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing

Muktafin¹,

Kusrini²,

Luthfi³

2020

eksplora

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Ulasan produk di marketplace merupakan informasi yang berharga apabila diolah dengan baik. Penjual dapat melakukan analisis ulasan produk untuk mendapat informasi yang dapat digunakan dalam evaluasi produk dan layanan. Kegiatan analisis ulasan produk tidak cukup dengan melihat jumlah bintang, diperlukan melihat seluruh isi komentar ulasan untuk dapat mengetahui maksud dari ulasan. Apabila dalam jumlah sedikit dapat dilakukan secara manual, namun dalam jumlah banyak lebih efektif menggunakan sistem. Dibutuhkan sistem yang mampu menganalisis banyak ulasan dengan efektif agar memudahkan dalam memahami maksud ulasan. Penelitian ini menggunakan algoritma KNN dan TF-IDF dengan pendekatan NLP untuk mengklasifikasikan ulasan produk “hijab instan” ke dalam 2 kelas (positif dan negatif). Klasifikasi menggunakan pendekatan NLP mendapat akurasi sebesar 76,92%, presisi 80,00% dan recall 74,07%, sedangkan tanpa NLP hanya mendapat akurasi sebesar 69,23%, presisi 80,00% dan recall 64,52%. Kata yang sering muncul pada ulasan dapat menggambarkan penilaian pembeli secara umum pada produk. Pada ulasan positif menunjukkan pembeli puas terhadap kualitas, kecepatan pengiriman dan harga barang, sedangkan pada ulasan negatif pembeli kecewa pada warna, dan jumlah barang yang dikirim tidak sama dengan yang dipesan.

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Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

Cited by 29 publications

References 9 publications

Brand Potential User Identification Algorithm Based on Sentiment Analysis

Brand Potential User Identification Algorithm Based on Sentiment Analysis

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing

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