Sentiment Analysis and Topic Modelling for Identification of Government Service Satisfaction

Aziz, Moh. Nasrul; Firmanto, Ari; Fajrin, Ahmad Miftah; Ginardi, Raden Venantius Hari

doi:10.1109/icitacee.2018.8576974

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…However, these studies were conducted using different workflows. Aziz et al (2018) conducted sentiment analysis and topic modeling on textual data obtained from Twitter to identify satisfaction with government service. Data was processed into information by grouping tweets into positive and negative classes.…”

Section: Methodsmentioning

confidence: 99%

“…Previous work on topic modeling and sentiment analysis has been carried out by Aziz et al (2018) to reveal information regarding the level of user satisfaction with Surabaya government services based on tweet data. Other research extracted useful information from a collection of tweet data to examine public opinion on Uber (Alamsyah et al 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques

Akbar

Santoso

Putra

et al. 2021

J. Sistem Inf. (J. Inf. Sys.)

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Availability of access to online learning platforms is expected to support online learning activities amid the COVID-19 pandemic. There are several issues experienced by students in online learning activities. These issues are related to Internet access, learning content, and learning evaluation. Online learning platforms are indispensable to be able to provide good learning resources that are easily accessible to students. This study aims to explore data reviews on Google Play Store to perceive the priority of service improvements that need to be carried out by online learning platform providers. Topic modeling and sentiment analysis are applied to extract useful information based on user sentiment towards the topics discussed in the application review. The result of topic analysis shows that topic trends in user reviews are about Live Class, Tryout, Subject Matter, User Account, Tutorial Video, and Free Learning Access. Meanwhile, the result of NRS assessment identified aspects that need to be a priority for improving online learning platform services. These aspects are in the Tryout and User Account sections. On the other hand, the aspect of Free Learning Access received the highest NRS. Users are helped by having free access to all learning content during learning from home activities.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques

Akbar

Santoso

Putra

et al. 2021

J. Sistem Inf. (J. Inf. Sys.)

View full text Add to dashboard Cite

show abstract

“…Most of the related previous works primarily use either only embeddings as text representation that are incorporated into the Sentiment Analysis model (e.g., [2,3]) or they consider Topic Modeling for determining the opinion by topic, and not to add context to the model (e.g., [16,17]).…”

Section: Related Workmentioning

confidence: 99%

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

et al. 2021

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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.

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“…[21] conducted numerical experiments on Health Care Reform and United States Presidential election debates in the year 2009 by taking polarity labeled datasets and proposed a novel SA on micro-blogs incorporating homophily [23] in social networks, additionally introducing topic context to cater the semantic relations between them. In 2007, [14], [24] presented a sentiment polarity based TM to analyze public satisfaction at the government in Surabaya city. A methodology based on "Markov chain" was proposed by [14] where all the words in the same sentence and its consecutive sentences falls under same topics and the model was trained and inferred using Hidden Markov tools.…”

Section: Related Workmentioning

confidence: 99%

T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT

Palani,

Rajagopal,

Pancholi

2021

Preprint

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Sentiment analysis (SA) has become an extensive research area in recent years impacting diverse fields including ecommerce, consumer business, and politics, driven by increasing adoption and usage of social media platforms. It is challenging to extract topics and sentiments from unsupervised short texts emerging in such contexts, as they may contain figurative words, strident data, and co-existence of many possible meanings for a single word or phrase, all contributing to obtaining incorrect topics. Most prior research is based on a specific theme/rhetoric/focused-content on a clean dataset. In the work reported here, the effectiveness of BERT(Bidirectional Encoder Representations from Transformers) in sentiment classification tasks from a raw live dataset taken from a popular microblogging platform is demonstrated. A novel T-BERT framework is proposed to show the enhanced performance obtainable by combining latent topics with contextual BERT embeddings. Numerical experiments were conducted on an ensemble with about 42000 datasets using NimbleBox.ai platform with a hardware configuration consisting of Nvidia Tesla K80(CUDA), 4 core CPU, 15GB RAM running on an isolated Google Cloud Platform instance. The empirical results show that the model improves in performance while adding topics to BERT and an accuracy rate of 90.81% on sentiment classification using BERT with the proposed approach.

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Sentiment Analysis and Topic Modelling for Identification of Government Service Satisfaction

Cited by 10 publications

References 17 publications

User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques

User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT

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