Comparison Method for Emotion Detection of Twitter Users

Güven, Zekeriya Anıl; Di̇ri̇, Banu; Çakaloğlu, Tolgahan

doi:10.1109/asyu48272.2019.8946435

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Several studies have been conducted on low-resource languages, showcasing diverse approaches and applications in the field. For instance, G"uven et al [30] utilized NMF and LDA algorithms to evaluate sentiment in Turkish tweets. Additionally, Habbat et al [31] focused on analyzing Moroccan tweets to extract relevant data, identify distinct moods, and illustrate frequently occurring subjects.…”

Section: Low-resource Languagesmentioning

confidence: 99%

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Karimi

Akbari

2023

Preprint

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Topic modeling is a popular natural language processing technique to uncover hidden patterns and topics in extensive text collections. However, there is a lack of comprehensive studies that focus specifically on applying topic modeling algorithms to short texts, particularly from social media platforms. Even fewer studies have explored comparing different topic modeling algorithms for low-resource languages such as Persian. Our study aims to address this gap by thoroughly investigating topic modeling algorithms and metrics tailored for short texts, particularly Persian tweets. We collected and preprocessed a substantial dataset of Persian tweets. We also developed a dedicated tool that enables reproducibility and facilitates the evaluation of various topic modeling algorithms using the provided dataset. Our comparative analysis included Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Latent Semantic Indexing (LSI), Gibbs Sampling Dirichlet Mixture Model (GSDMM), and Correlated Topic Model (CTM). To measure their performance, we employed well-established metrics, namely Purity, Normalized Mutual Information (NMI), and Coherence. Our experimental results indicate that GSDMM and CTM+BERT exhibit superior performance compared to other algorithms in terms of purity and NMI on the Persian short-text topic modeling dataset. Additionally, CTM+BERT demonstrates competitive coherence performance compared to GSDMM. Our study provides valuable insights into the effectiveness of different topic modeling approaches for short texts and can help researchers select the most appropriate algorithm for their specific use case.

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Section: Low-resource Languagesmentioning

confidence: 99%

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Karimi

Akbari

2023

Preprint

View full text Add to dashboard Cite

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“…There are several previous studies related to topic modelling with the Non-negative Matrix Factorization method, namely research (Guven, 2019) evaluation of the NMF and LDA methods using the Turkish Twitter dataset, which shows the NMF accuracy level is higher than LDA, where NMF produces an accuracy rate of 97.8 %, while LDA produces an accuracy rate of 96.0% [5]. Research (Nakhon Pathom, 2014) conducted topic modelling on a collection of comments contained on yahoo news social media using the Latent Dirichlet Allocation and Non-negative Matrix Factorization methods, which resulted in extracting three topics, with ten keywords for each topic and limited to a maximum keyword of 10,000 in comments body [6].…”

Section: Introductionmentioning

confidence: 99%

Topic Modelling Using Non-Negative Matrix Factorization (NMF) for Telkom University Entry Selection from Instagram Comments

Alfajri

Richasdy

Bijaksana

2022

JoSYC

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The development of information technology is increasingly rapid, such as social media, which has much influence. Social media is a place or media used to express and express various opinions on a topic. One example is Instagram. Instagram is a social media platform with many features, such as posting photos, videos, comments, likes, and others. The comments feature that Instagram has contained much public opinion that can be used as data. Nothing but the post on the SMB Telkom University Instagram account about the entrance to the university. In posts about the entrance to Telkom university, many Instagram users comment on the post. This can be convenient for the marketing team to get topics or discussions that most followers need from Telkom University's Instagram account. Therefore, a topic modelling of Instagram users' perceptions of comments posted on the entrance to Telkom university was carried out using the Nonnegative Matrix Factorization (NMF) method. After doing several research scenarios, the best coherent value was obtained with a coherent value of 0.60628 and the best 4 topics.

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Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

Modi

Shah

et al. 2022

Ann. Data. Sci.

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In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

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Comparison Method for Emotion Detection of Twitter Users

Cited by 3 publications

References 11 publications

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Comparative Analysis of Topic Modeling Algorithms for Short Texts in Persian Tweets

Topic Modelling Using Non-Negative Matrix Factorization (NMF) for Telkom University Entry Selection from Instagram Comments

Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

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