A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

Chu, Kah Em; Keikhosrokiani, Pantea; Asl, Moussa Pourya

doi:10.47836/pjst.30.4.14

Cited by 18 publications

(3 citation statements)

References 37 publications

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“…Research carried out by Min and Zulkarnain [20], where experts in psychology and human development were involved for setting up the ground truth on tweets, concluded that VADER has a higher accuracy of 0.79% as compared to TextBlob, which has an accuracy of 0.73%. Since VADER does not offer a subjectivity score [21], it was utilized solely for polarity comparison purposes. Data manipulation and analysis was carried out by using the Python Pandas open-source library.…”

Section: Methodsmentioning

confidence: 99%

Polarizing Topics on Twitter in the 2022 United States Elections

Katalinić,

Dunđer,

Seljan

2023

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Politically polarizing issues are a growing concern around the world, creating divisions along ideological lines, which was also confirmed during the 2022 United States midterm elections. The purpose of this study was to explore the relationship between the results of the 2022 U.S. midterm elections and the topics that were covered during the campaign. A dataset consisting of 52,688 tweets in total was created by collecting tweets of senators, representatives and governors who participated in the elections one month before the start of the elections. Using unsupervised machine learning, topic modeling is built on the collected data and visualized to represent topics. Furthermore, supervised machine learning is used to classify tweets to the corresponding political party, whereas sentiment analysis is carried out in order to detect polarity and subjectivity. Tweets from participating politicians, U.S. states and involved parties were found to correlate with polarizing topics. This study hereby explored the relationship between the topics that were creating a divide between Democrats and Republicans during their campaign and the 2022 U.S. midterm election outcomes. This research found that polarizing topics permeated the Twitter (today known as X) campaign, and that all elections were classified as highly subjective. In the Senate and House elections, this classification analysis showed significant misclassification rates of 21.37% and 24.15%, respectively, indicating that Republican tweets often aligned with traditional Democratic narratives.

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Section: Methodsmentioning

confidence: 99%

Polarizing Topics on Twitter in the 2022 United States Elections

Katalinić,

Dunđer,

Seljan

2023

Information

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“…In recent years, text mining and sentiment analysis have experienced significant advancements and widespread adoption across various industries. With the exponential growth of digital content and the increasing reliance on social media platforms, there has been a surge in the volume of textual data available for analysis [1]. Text mining techniques, including natural language processing (NLP) and machine learning algorithms, have become more sophisticated, enabling the extraction of valuable insights from unstructured text data.…”

Section: Introductionmentioning

confidence: 99%

A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis

2024

jes

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A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of English and American literature. This paper explores the application of text mining and sentiment analysis techniques to analyze a dataset comprising American literary works. With computational methods such as bi-gram analysis, multimodal feature extraction, and sentiment analysis using the Bi-gram Multimodal Sentimental Analysis (Bi-gramMSA) approach. With the proposed Bi-gramMSA model the multimodal features in the American Literature are examined to investigate the thematic, emotional, and multimodal aspects of the literature. Through our analysis, we uncover significant bi-grams, extract multimodal features, and assess sentiment distribution across the texts. The results highlight the effectiveness of these computational methodologies in uncovering patterns, sentiments, and features within the literary corpus. The proposed Bi-gramMSA model achives a higher score for the different scores in the Chinese Literature.

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“…Natural language processing (NLP) techniques have been widely adopted for text classification, which assigns labels to sentences, paragraphs, or documents (Abadah et al, 2023;Asri et al, 2022;Chu et al, 2022;Fasha et al, 2022;Jafery et al, 2023;John and Keikhosrokiani, 2022;Al Mamun et al, 2022). This technique has been applied to a variety of fields, such as health, social science, business marketing and law.…”

Section: Introductionmentioning

confidence: 99%

Decoding violence against women: analysing harassment in middle eastern literature with machine learning and sentiment analysis

Low,

Keikhosrokiani,

Pourya Asl

2024

Humanit Soc Sci Commun

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The rising prevalence of harassment in Middle Eastern countries is mirrored in literary works from the region. However, extracting data from these texts to understand the typology and frequency of the cases poses a significant challenge due to human cognitive limitations and potential biases. Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The data source for this study consists of twelve Middle Eastern novels. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model. Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Finally, a long short-term memory-gated recurrent unit (LSTM-GRU) deep learning model is built to classify the sentiment characteristics that induce sexual harassment. The proposed model achieved an accuracy of 75.8% while outperforming five other algorithms. Additionally, a sentiment classification with three labels—negative, positive, and neutral—was developed using an LSTM-GRU RNN deep learning model. The accuracy of this model was 84.5%. Most statements, even those involving physical sexual harassment, which had greater levels of sexual harassment, had negative sentiments, according to lexicon-based sentiment analysis. This study contributes to the field of text mining by providing a novel approach to identifying instances of sexual harassment in literature in English from the Middle East. The use of machine learning models and sentiment analysis techniques allows for more accurate identification and classification of different types of sexual harassment. Furthermore, this study sheds light on the prevalence of sexual harassment in Middle Eastern countries and highlights the need for further research and action to address this issue.

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A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

Cited by 18 publications

References 37 publications

Polarizing Topics on Twitter in the 2022 United States Elections

Polarizing Topics on Twitter in the 2022 United States Elections

A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis

Decoding violence against women: analysing harassment in middle eastern literature with machine learning and sentiment analysis

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