Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity

2021 ◽  
Vol 5 (3) ◽  
pp. 520-526
Author(s):  
Irbah salsabila ◽  
Yuliant Sibaroni
Keyword(s):  
Support Vector Machine ◽  
Semantic Similarity ◽  
Support Vector ◽  
Product Reviews ◽  
Important Requirement ◽  
Test Result ◽  
Beauty Products

Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the preprocessing stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

2021 ◽  
Author(s):  
Dimple Chehal ◽  
Parul Gupta ◽  
Payal Gulati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Product Reviews ◽  
K Nearest Neighbor ◽  
Customer Reviews ◽  
Percent Accuracy

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

scholarly journals Identifikasi Cyberbullying pada Kolom Komentar Instagram dengan Metode Support Vector Machine dan Semantic Similarity

2020 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Lintani Afina Hajar Raudhoti ◽  
Anisa Herdiani ◽  
Ade Romadhony
Keyword(s):  
Support Vector Machine ◽  
Semantic Similarity ◽  
Support Vector ◽  
E Mail

Instagram merupakan laman media sosial berbagi foto dan video. Pengguna instagram biasanya melakukan aktivitas seperti mengunggah foto, saling mengikuti, menyukai hingga mengomentari setiap unggahan foto dan video. Namun, popularitas media sosial ini tidak lepas dari fenomena cyberbullying. Cyberbullying dapat didefinisikan sebagai penyalahgunaan teknologi melalui ponsel, e-mail, ruang berbicara atau sosial media untuk mempermalukan atau mengancam orang lain. Komentar yang termasuk kategori cyberbullying dapat menimbulkan efek negatif, terutama pada pihak yang diserang. Oleh karena itu, penelitian untuk mengidentifikasi kalimat cyberbullying menjadi hal yang penting. Identifikasi kalimat cyberbullying dapat dilakukan dengan pembelajaran mesin yang melibatkan pengetahuan korpus. Tugas Akhir ini menggunakan metode pembelajaran mesin Supoort Vector Machine (SVM) untuk dapat mengidentifikasi kalimat yang mengandung cyberbullying dan tidak. Akan tetapi, penggunaan metode klasifikasi SVM saja mempunyai kekurangan pada kondisi data uji yang mengandung kata-kata yang tidak terdapat pada data latih. Penambahan informasi kata-kata lain yang terkait secara semantik dapat meningkatkan performansi. Oleh karena itu, perlu ditambahkan informasi semantik keterkaitan antar kata yang diambil dari kamus untuk dapat meningkatkan akurasi identifikasi. Hasil yang diperoleh menunjukkan bahwa penambahan informasi semantik dapat meningkatkan performansi berupa akurasi pada tahap pengujian. Angka kenaikan yang diperoleh sebanyak 7% dari 67% menjadi 74%.

scholarly journals Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

2020 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Classification Performance ◽  
Support Vector ◽  
Product Reviews ◽  
Feature Selection Technique ◽  
Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.

scholarly journals A Comparative Study of Different Classification Techniques for Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 49-57
Author(s):  
Soumadip Ghosh ◽  
Arnab Hazra ◽  
Abhishek Raj
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Product Reviews ◽  
Accurate Model

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.

scholarly journals BUYER RATING AND SENTIMENT ANALYSIS USING DISCRIMINATORY SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Opinion Mining ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Product Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Customer Feedback

This substantial issue is increasingly important in business and culture. It presents many challenging research scenarios but guarantees a relevant insight for everybody interested in view evaluation and social networking analysis. This paper's key aim is to detect sentiment polarity such as positive, negative, and emoji representation with customer feedback on various products. Opinion mining from e-commerce sites has a significant part in making purchase decisions and founders to boost their product and marketing strategies. But, it becomes very difficult for the clients to understand and assess the product's actual view manually. Because of this, we need an automated way. The majority of the researchers used machine learning algorithms to do an automated representation of phrase embedding. Among the popular techniques in machine learning has been used the support vector machine (SVM). The weighted support vector machine (WSVM) is the improved version for the standard SVM to grow the outlier sensitivity issue. In this paper, the word2Vec version uses to extract the attributes from customer reviews in WSVM based on opinion analysis of product reviews in E-commerce websites. The experiment result shows that the suggested WSVM can works better on the opinion classification job doing any version applied.

A Model for Predicting the Wear Degree of Electrode Tip

2014 ◽  
Vol 574 ◽  
pp. 292-297
Author(s):  
Jin Zhang ◽  
Peng Xian Zhang ◽  
Xiang Jian Xu
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Digital Image ◽  
Model Prediction ◽  
Support Vector ◽  
Laser Measurement ◽  
Characteristic Parameters ◽  
Laser Measurement System ◽  
Image Characteristic ◽  
Test Result

A new method is put forward to predicting the degree of electrode tip wear based on a laser measurement and digital image of the surface joint indentation. First, in order to monitoring the degree of electrode tip wear, the decline altitudes of sphere ΔH that can indicate variation of electrode tip shape are measured by means of the laser measurement system. Second, through the correlation analysis between the parameters S0, S1, S, K1 reflecting digital image characteristic of joint indentation and the decline altitudes of sphere ΔH, S0, S, K1 are extracted as characteristic parameters of monitoring electrode tip wear. At last, a model of support vector machine (SVM) for predicting the degree of electrode tip wear is established between the parameters S0, S, K1 as the input vector and ΔH as the target vector. Test result shows, the correlation coefficient between model prediction and actual measured values are 0.9907. The prediction model can realize estimating the degree of electrode tip wear.

scholarly journals Product Review Mining for Opinion Using Ensemble Classifier

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
G. Vinodhini ◽  
RM. Chandrasekaran
Keyword(s):  
Support Vector Machine ◽  
Opinion Mining ◽  
Online Reviews ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Product Reviews ◽  
Operating Characteristics ◽  
Product Review ◽  
Review Mining ◽  
Suitable Form

Online product reviews is considered as a major informative resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volume of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve.   Key words: Opinion, Classification, Machine Learning.

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