Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN

Dengan semakin berkembangnya teknologi informasi, maka muncul istilah e-commerce dalam dunia bisnis. Pada e-commerce ada fitur review, pelanggan dapat memberikan review berupa teks, gambar, dan bintang. Review tersebut merupakan opini dari pelanggan terkait barang yang dibeli. Tetapi pada kebanyakan e-commerce tidak ada fitur kategori terkait review hal ini membuat calon pembeli kesusahan dalam menganalisa secara manual. Aspect-based sentiment analysis (ABSA) merupakan solusi dari permasalahan tersebut. ABSA memiliki tiga tugas salah satunya Aspect Category Detection yang memiliki fungsi untuk menggabungkan review pelanggan menjadi beberapa aspek dimana aspek-aspek tersebut sudah didefinisikan terlebih dahulu. Cukup banyak penelitian terkait Aspect Category Detection dengan mengunakan machine learning. Dari beberapa metode yang diuji, Convolutional Neural Network (CNN) merupakan metode terbaik. Selain itu penggunaan BERT sebagai word embedding menghasilkan output yang bagus baik dari pada word embedding konvensional. Penelitian ini menggunakan dataset dari e-commerce Bukalapak dengan 3114 review dan 6 aspek (Akurasi, Pengiriman, Kualitas, Harga, Pengemasan, dan Pelayanan). Berdasarkan ujicoba dengan menggunakan IndoBERT sebagai word embedding dan CNN untuk deteksi aspek, maka didapatkan akurasi sebesar 94,86%. Dengan demikian model tersebut dapat digunakan untuk deteksi aspek. Selain itu, metode CNN mendapatkan hasil yang lebih baik dari pada metode LSTM

Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN

Dengan semakin berkembangnya teknologi informasi, maka muncul istilah e-commerce dalam dunia bisnis. Pada e-commerce ada fitur review, pelanggan dapat memberikan review berupa teks, gambar, dan bintang. Review tersebut merupakan opini dari pelanggan terkait barang yang dibeli. Tetapi pada kebanyakan e-commerce tidak ada fitur kategori terkait review hal ini membuat calon pembeli kesusahan dalam menganalisa secara manual. Aspect-based sentiment analysis (ABSA) merupakan solusi dari permasalahan tersebut. ABSA memiliki tiga tugas salah satunya Aspect Category Detection yang memiliki fungsi untuk menggabungkan review pelanggan menjadi beberapa aspek dimana aspek-aspek tersebut sudah didefinisikan terlebih dahulu. Cukup banyak penelitian terkait Aspect Category Detection dengan mengunakan machine learning. Dari beberapa metode yang diuji, Convolutional Neural Network (CNN) merupakan metode terbaik. Selain itu penggunaan BERT sebagai word embedding menghasilkan output yang bagus baik dari pada word embedding konvensional. Penelitian ini menggunakan dataset dari e-commerce Bukalapak dengan 3114 review dan 6 aspek (Akurasi, Pengiriman, Kualitas, Harga, Pengemasan, dan Pelayanan). Berdasarkan ujicoba dengan menggunakan IndoBERT sebagai word embedding dan CNN untuk deteksi aspek, maka didapatkan akurasi sebesar 94,86%. Dengan demikian model tersebut dapat digunakan untuk deteksi aspek. Selain itu, metode CNN mendapatkan hasil yang lebih baik dari pada metode LSTM

A Survey of Sentiment Analysis and Sarcasm Detection: Challenges, Techniques, and Trends

In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data

FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE

Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets

Modified EDA and Backtranslation Augmentation in Deep Learning Models for Indonesian Aspect-Based Sentiment Analysis

In the process of developing a business, aspect-based sentiment analysis (ABSA) could help extract customers' opinions on different aspects of the business from online reviews. Researchers have found great prospective in deep learning approaches to solving ABSA tasks. Furthermore, studies have also explored the implementation of text augmentation, such as Easy Data Augmentation (EDA), to improve the deep learning models’ performance using only simple operations. However, when implementing EDA to ABSA, there will be high chances that the augmented sentences could lose important aspects or sentiment-related words (target words) critical for training. Corresponding to that, another study has made adjustments to EDA for English aspect-based sentiment data provided with the target words tag. However, the solution still needs additional modifications in the case of non-tagged data. Hence, in this work, we will focus on modifying EDA that integrates POS tagging and word similarity to not only understand the context of the words but also extract the target words directly from non-tagged sentences. Additionally, the modified EDA is combined with the backtranslation method, as the latter has also shown quite a significant contribution to the model’s performance in several research studies. The proposed method is then evaluated on a small Indonesian ABSA dataset using baseline deep learning models. Results show that the augmentation method could increase the model’s performance on a limited dataset problem. In general, the best performance for aspect classification is achieved by implementing the proposed method, which increases the macro-accuracy and F1, respectively, on Long Short-Term Memory (LSTM) and Bidirectional LSTM models compared to the original EDA. The proposed method also obtained the best performance for sentiment classification using a convolutional neural network, increasing the overall accuracy by 2.2% and F1 by 3.2%. Doi: 10.28991/ESJ-2023-07-01-018 Full Text: PD

Sentence-Level Granularity Oriented Sentiment Analysis of Social Media Using Long Short-Term Memory (LSTM) and IndoBERTweet Method

The dissemination of information through social media has been rampant, especially on the Twitter platform. This information eventually invites various opinions from users as their points of view on a topic being discussed. These opinions can be collected and processed using sentiment analysis to assess public tendencies to obtain a fundamental source of decision-making. However, the procedure is not optimal enough due to its inability to recognize the word meaning of the opinion sentences. By using sentence-level granularity-oriented sentiment analysis, the system can explore the "sense of the word" in each sentence by giving it a granularity weight as the system's consideration in recognizing word meaning. To construct the procedure, this research utilizes LSTM as the classification model combined with TF-IDF and IndoBERTweet as feature extraction. Not only that, but this research also conducts the Word2Vec feature expansion method which was built using Twitter and IndoNews corpus to produce word similarity corpus and find effective word semantics. To be fully compliant with the granularity requirements, manual labeling, and system labeling were performed by considering weight granularity as a model performance comparison. This research succeeded in getting 88.97% accuracy for manual labeling data and 97.80% for system labeling data after combining these methods. The experimental results show that the granularity-oriented sentiment analysis model can outperform the conventional sentiment analysis system which can be seen based on the high performance of the resulting system

Challenges of Sarcasm Detection for Social Network : A Literature Review

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance

Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model

Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature representation to be combined with convolutional neural network-extreme gradient boosting (CNN-XGBoost). Second, IndoBERT is used both as the feature representation as well as the classifier to directly solve the classification task. Additional analysis is performed to compare our results with those using multilingual BERT model. According to our experimental results, our first model using IndoBERT as feature representation shows significant performance over some baselines. Our second model using IndoBERT as both feature representation and classifier can significantly enhance the effectiveness of our first model. In summary, our proposed models can improve the effectiveness of the baseline using Word2Vec-CNN-XGBoost by 19.19% and 6.17%, in terms of accuracy and F-1 score, respectively