Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

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Sentiment Analysis of Lithuanian Texts Using Deep Learning Methods

Communications in Computer and Information Science - Information and Software Technologies ◽

10.1007/978-3-319-99972-2_43 ◽

2018 ◽

pp. 521-532 ◽

Cited By ~ 1

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Learning Methods

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Enhanced news sentiment analysis using deep learning methods

Journal of Computational Social Science ◽

10.1007/s42001-019-00035-x ◽

2019 ◽

Vol 2 (1) ◽

pp. 33-46 ◽

Cited By ~ 18

Author(s):

Wataru Souma ◽

Irena Vodenska ◽

Hideaki Aoyama

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Learning Methods ◽

News Sentiment

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Feature Extraction through Sentiment Analysis of Tourist Sentiments using Deep Learning Techniques like CNN, RNN and LSTM

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2846.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 2254-2261

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

High Efficiency ◽

Learning Algorithms ◽

Neutral Type ◽

Tourism Industry ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Learning Techniques

Sentiments are the emotions which are communicated among individuals. These are opinions given by people on any item, product or service availed or experience online. This paper discusses that part of research area which involves the analysis of sentiments exchanged by people online that further tells how sentiments and features through online tourist reviews are extracted using deep learning techniques. Tourist behavior can be judged by tourists reviews for various tourist places, hotels and other services provided by tourism industry. The proposed idea of the paper is to show the high efficiency of deep learning techniques like CNN, RNN,LSTM to extract the features online by use of extra hidden layers. Further, comparison of these techniques as well as comparison of these techniques with machine learning classical algorithms like SVM, Naïve Bayes, KNN,RF etc has been done to show that deep learning methods are more efficient than classical machine learning algorithms. The accurate capturing of attitudes of tourists towards tourist places, hotels & other services of tourism industry plays utmost important role to enhance the business model of tourism industry. This can be done through sentiment analysis using deep learning methods efficiently. Classification of polarity will be done by extracting textual features using CNN,RNN,LSTM deep learning algorithms. Extracting features are fed to deep learning classifier to classify the review into either positive, negative or neutral type of reviews. After comparing various deep learning and classical techniques of machine learning, it has been concluded that LSTM,RNN give best results to classify reviews into positive and negative reviews rather than SVM,KNN classical techniques. In this way sentiment analysis has been done and the proposed idea of this research paper is change in the machine learning techniques or methods from classical algorithms to neural network deep learning methods which in future definitely will give better results to analyze deeply the sentiments of tourists to find out the liking and disliking of various tourist places, hotels and related tourism services that will help tourism business industry to work on the gap in existing services provided by them and system can become more efficient in future. Such improved tourism system will give benefits to tourists or users in terms of better services and undoubtedly it will help tourism industry to enhance business in future.

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Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v6i3.1300 ◽

2021 ◽

Author(s):

Siwi Cahyaningtyas ◽

Dhomas Hatta Fudholi ◽

Ahmad Fathan Hidayatullah

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Data Preprocessing ◽

Sentiment Classification ◽

Accuracy Score ◽

Learning Methods ◽

Average Accuracy ◽

Quality Of Products

Tourism is one of the fastest-growing industries. Many travelers book hotels and share their experiences using travel e-commerce sites. To improve the quality of products and services, we can take advantage by analyzing their reviews. We can see the good and the bad thing reviews in every aspect of the hotel. However, research to analyze sentiment in every aspect using Indonesian hotel reviews is still relatively new. In this work, we propose to create an Aspect-based Sentiment Analysis (ABSA) using Indonesian hotel reviews to solve the problem. This research consists of four steps: collecting data, preprocessing, aspect classification, and sentiment classification. Our classification process compares with eight deep learning methods (RNN, LSTM, GRU, BiLSTM, Attention BiLSTM, CNN, CNN-LSTM, and CNN-BiLSTM). In aspect classification, we have six classes of aspects which are harga (price), hotel, kamar (room), lokasi (location), pelayanan (service), and restoran (restaurant). In sentiment analysis, we compared two scenarios to classify sentiments as positive or negative. The first one is to classify sentiment in all aspects, and the second one is to classify sentiment in every aspect. The results showed that LSTM achieved the best model for aspect classification with an accuracy value of 0.926. For sentiment classification, our experiments showed that classify sentiment in every aspect achieved a better result than classify sentiment in all aspects. The result showed that the CNN model gets an average accuracy score of 0.904.

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