Sentiment Analysis and Topic Modelling Using the LDA Method related to the Flood Disaster in Jakarta on Twitter

Rahmadan, M. Choirul; Hidayanto, Achmad Nizar; Ekasari, Dika Swadani; Purwandari, Betty; Theresiawati, - -

doi:10.1109/icimcis51567.2020.9354320

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…3, Health domain papers accounted for five papers of the final selection [10]- [14]. Then followed by four papers in political domains [13], [15]- [17], two papers about App [7], [8], and three papers in general domain [18]- [20], as well as one each on another domain: education [21], regulation [22], disaster [23], and tourism [24]. The general domain is public sentiment without a specific topic taken within a particular time.…”

Section: Resultsmentioning

confidence: 99%

“…4, the majority of the data for the sentiment analysis came from social media, especially Twitter. Twelve paper datasets were from Twitter [10], [11], [23], [24], [12]- [14], [16]- [19], [22], and one paper datasets each was from Instagram comments [15], student feedback [21], research answers [20], google play site [7] and play store [8].…”

Section: Rq2: What Data Sources Are Used In Sentiment Analysis Studie...mentioning

confidence: 99%

“…Twitter data crawling is done in several ways. According to [11], [14], [19], [23], they used the Twitter API that provides unique and advanced programmatic access to Twitter. Other studies used the python library for data crawlings, such as Tweepy, Twint, and GetOldTweets.…”

Section: Fig 4 Data Sources Used In Sentiment Analysis Studies Using ...mentioning

confidence: 99%

See 2 more Smart Citations

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Rufaida,

Permanasari,

Setiawan

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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Sentiment analysis is one of the most exciting topics to research in text mining. Lexicon-based sentiment analysis produces good results across a wide range of conversational topics, is easily improved using various sources of information, and does not require additional training. However, only a few review papers have addressed sentiment analysis with specific lexicon dictionaries. As a result, neither data sources nor pre-processing techniques specific to a lexicon dictionary are discussed further. A Systematic Literature Review was conducted to provide a comprehensive and structured lexicon-based sentiment analysis using the Inset dictionary. The literature review resulted in selecting seventeen papers for a detailed study. The findings show that in the last five years, most sentiment analysis research using Inset focused on the health domain. Twitter provides the majority of the data for sentiment analysis. Stopword removal, tokenization, case folding, and stemming are common pre-processing techniques. This study also contained some additional observations from completed research.

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Section: Resultsmentioning

confidence: 99%

Section: Rq2: What Data Sources Are Used In Sentiment Analysis Studie...mentioning

confidence: 99%

See 1 more Smart Citation

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Rufaida,

Permanasari,

Setiawan

2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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show abstract

“…In recent years, it has gained significant popularity due to its efficiency in handling large volumes of unstructured text data and its ability to automatically identify patterns and themes within that data. It has a wide range of applications, including information retrieval, document classification, sentiment analysis, and social media analysis [11]- [13].…”

Section: Related Work 21 Topic Modelingmentioning

confidence: 99%

Hybrid model for extractive single document summarization: utilizing BERTopic and BERT model

Maryanto,

Philips,

Suganda Girsang

2024

IJ-AI

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Extractive text summarization has been a popular research area for many years. The goal of this task is to generate a compact and coherent summary of a given document, preserving the most important information. However, current extractive summarization methods still face several challenges such as semantic drift, repetition, redundancy, and lack of coherence. A novel approach is presented in this paper to improve the performance of an extractive summarization model based on bidirectional encoder representations from transformers (BERT) by incorporating topic modeling using the BERTopic model. Our method first utilizes BERTopic to identify the dominant topics in a document and then employs a BERT-based deep neural network to extract the most salient sentences related to those topics. Our experiments on the cable news network (CNN)/daily mail dataset demonstrate that our proposed method outperforms state-of-the-art BERT-based extractive summarization models in terms of recall-oriented understudy for gisting evaluation (ROUGE) scores, which resulted in an increase of 32.53% of ROUGE-1, 47.55% of ROUGE-2, and 16.63% of ROUGE-L when compared to baseline BERT-based extractive summarization models. This paper contributes to the field of extractive text summarization, highlights the potential of topic modeling in improving summarization results, and provides a new direction for future research.

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“…This method is useful for modeling documents that arise from several topics, where a topic is defined as a distribution of fixed vocabulary terms [5]. In a study conducted by M. Choirul Rahmadan, Achmad Nizar Hidayanto, et al, regarding sentiment analysis and topic modeling related to flooding that occurred in Jakarta using the LDA method [6],this study obtained 9 optimum topics.…”

Section: Introductionmentioning

confidence: 99%

Telkom University Opinion Topic Modeling on Twitter Using Latent Dirichlet Allocation During Covid-19 Pandemic

Pratama

Richasdy²,

Purbolaksono³

2022

mib

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In the current digital era, the development of information technology is growing rapidly. The development of information technology is followed by the development of social media, one of the social media that is on the rise is Twitter. Because there are many Twitter users around the world, Twitter stores a lot of data that can be used for something, one of which is to determine the category of public opinion about a company or university, in this study the focus is more on the category of public opinion about Telkom University. The public opinion can be grouped or categorized to make it easier to determine the topic being discussed. Determining opinions manually will take a long time due to the large number of tweets. Therefore, there must be another method to determine the categories of public opinion on Twitter. One of them is the Latent Dirichlet Allocation (LDA) method with a dataset of tweets of Indonesian-language Twitter users. With this method, grouping tweets on a large scale is more efficient. From the modeling made, the most optimum results obtained with a coherence score using the c_umass method of -15.33029 with a combination of 9 topics, 0.31 alpha value, and 0.01 beta value.

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Sentiment Analysis and Topic Modelling Using the LDA Method related to the Flood Disaster in Jakarta on Twitter

Cited by 12 publications

References 15 publications

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Lexicon-Based Sentiment Analysis Using Inset Dictionary: A Systematic Literature Review

Hybrid model for extractive single document summarization: utilizing BERTopic and BERT model

Telkom University Opinion Topic Modeling on Twitter Using Latent Dirichlet Allocation During Covid-19 Pandemic

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