Author: Denis Avetisyan
A new analysis of social media data reveals the emotional landscape of Bangladesh’s recent mass uprising, offering insights into public sentiment during a period of intense political and social change.
This research introduces a novel Bangla dataset and employs machine learning techniques to analyze public sentiment – including outrage, hope, and despair – expressed on Facebook during the 2024 Bangladesh uprising.
While sentiment analysis increasingly informs understanding of public opinion, its application to rapidly unfolding sociopolitical crises, particularly in non-English languages, remains largely unexplored. This study, ‘When a Nation Speaks: Machine Learning and NLP in People’s Sentiment Analysis During Bangladesh’s 2024 Mass Uprising’, addresses this gap by pioneering Bangla-specific sentiment analysis of social media data collected during a period of national unrest. Analysis of over 2,000 annotated Facebook headlines revealed prevalent emotions of outrage, hope, and despair, alongside key themes like corruption and protest, with language-specific models outperforming multilingual alternatives. How can these insights into collective emotional responses inform crisis communication and contribute to a more nuanced understanding of political upheaval?
Decoding the Digital Uprising: Sentiment in a Time of Disruption
The 2024 Bangladesh Uprising rapidly transformed Facebook into a contested information space, marked by a surge in emotionally charged content and the swift dissemination of narratives. Initial reports indicate a proliferation of unverified claims, alongside state-sponsored counter-messaging, creating a highly polarized digital landscape. This volatile environment wasn’t simply a reflection of events unfolding offline; it actively shaped perceptions, fueling both support for and opposition to the uprising. The sheer volume of posts, comments, and shares overwhelmed traditional fact-checking mechanisms, allowing misinformation to gain traction and contribute to a climate of distrust. Consequently, analyzing the sentiment expressed on Facebook during this period provides a critical window into understanding how the uprising was perceived, interpreted, and ultimately, how it unfolded – not just in the streets, but within the collective consciousness of a digitally connected nation.
Accurately gauging public sentiment during the 2024 Bangladesh Uprising is paramount to deciphering the complex socio-political forces at work. Shifts in emotional expression online – ranging from anger and fear to hope and solidarity – served as a barometer of evolving public opinion and provided crucial insights into the motivations driving participation in the uprising. Analyzing this sentiment, through methods like natural language processing of social media data, allows researchers to map the spread of information, identify key grievances, and understand how narratives surrounding the uprising were constructed and contested. Furthermore, tracking the intensity and direction of public feeling offers a vital window into the legitimacy – or lack thereof – perceived by the populace regarding both the government’s actions and the protesters’ demands, ultimately illuminating the underlying causes and potential long-term consequences of the unrest.
The 2024 Bangladesh Uprising coincided with, and was significantly shaped by, a widespread Internet Blackout imposed by authorities. This disruption to connectivity wasn’t simply a matter of limited access; it actively molded the narrative surrounding the events. With mainstream news outlets restricted and social media platforms throttled, citizens relied on circumvention tools – VPNs and proxy servers – to both access information and disseminate it, creating fragmented and often unreliable streams of data. This digital scarcity fostered the rapid spread of rumors and unverified claims, while simultaneously hindering efforts to document and verify events on the ground. The blackout thus transformed online discourse, prioritizing personal accounts and eyewitness reports, but also amplifying misinformation and making objective analysis considerably more challenging for both researchers and the public. Consequently, understanding the uprising requires acknowledging not just what information circulated, but how its restricted flow fundamentally altered the character of online communication.
Introducing the Bangla Political Sentiment Dataset: A Grounded Resource
The Bangla Political Sentiment Dataset consists of 2028 Facebook headlines sourced from publicly available posts. Each headline has been manually annotated with one of three distinct emotional labels: Outrage, Hope, or Despair. This annotation process provides a granular emotional categorization, allowing for focused analysis of sentiment expression within Bangla-language political discourse. The dataset is designed to facilitate research in areas such as sentiment analysis, emotion detection, and socio-political trend identification, specifically within the context of Bangla-language social media.
The Bangla Political Sentiment Dataset distinguishes itself through its concentrated focus on a single socio-political event, allowing for a nuanced understanding of public reaction to specific occurrences. Unlike broad sentiment analyses, this dataset doesn’t aggregate sentiment across disparate topics; rather, it provides labeled data tied to a defined context. Furthermore, the annotation scheme extends beyond simple positive/negative classifications by utilizing a granular three-class system – Outrage, Hope, and Despair – enabling more precise sentiment modeling and a deeper examination of emotional responses beyond basic polarity.
The Bangla Political Sentiment Dataset is constructed entirely from text in the Bangla language, also known as Bengali. This linguistic foundation presents specific challenges for sentiment analysis, as most pre-trained models are optimized for English or other widely-used European languages. Effective processing requires models capable of handling the morphological complexity of Bangla, including its rich inflectional system and postpositional grammar. Furthermore, nuanced understanding demands consideration of code-mixing, a common feature of online Bangla text where English words are frequently integrated. Consequently, models utilized with this dataset must either be specifically trained on Bangla text or employ techniques like transfer learning to adapt to the unique characteristics of the language.
Evaluating Sentiment Classification: A Multi-Model Approach
Transformer-based models were central to our sentiment classification evaluation, specifically examining the performance of XLM-RoBERTa, mBERT, and BanglaBERT. These models utilize the transformer architecture, enabling them to process sequential data like text and capture contextual relationships between words. XLM-RoBERTa is a multilingual model pre-trained on a large corpus of text in 100 languages, while mBERT is another multilingual model designed for cross-lingual understanding. BanglaBERT is specifically pre-trained on a large Bangla text corpus, making it suitable for sentiment analysis of Bangla text data. Each model was fine-tuned on the sentiment classification task to assess its ability to accurately categorize text based on expressed sentiment.
Classical machine learning algorithms, specifically Support Vector Machines (SVM) and Logistic Regression, were included in the sentiment classification evaluation to provide a baseline comparison against more recent transformer-based models. These algorithms required feature engineering; therefore, Term Frequency-Inverse Document Frequency (TF-IDF) weighted bigrams were utilized for feature extraction. TF-IDF assigns weights to terms based on their frequency within a document and inverse document frequency across the corpus, effectively highlighting important terms. The use of bigrams, rather than unigrams, captures contextual information by considering pairs of consecutive words, potentially improving the model’s ability to discern sentiment. This approach transforms the textual data into a numerical representation suitable for input into the SVM and Logistic Regression classifiers.
The zero-shot sentiment classification performance of several large language models was evaluated, revealing that DeepSeek-R1 achieved the highest accuracy at 74.0%. BanglaBERT followed closely with an accuracy of 72.0%, and XLM-RoBERTa achieved 71.0%. These results demonstrate the capacity of these models to perform sentiment analysis without task-specific training data, providing a baseline for comparison against models requiring fine-tuning.
Uncovering the Narrative Landscape: Topic Modeling and Sentiment
Latent Dirichlet Allocation (LDA), a probabilistic topic modeling technique, was instrumental in discerning the underlying thematic structure of the Bangla Political Sentiment Dataset. This unsupervised machine learning approach automatically identified prevalent topics within the collection of text data, treating each document as a mixture of these themes and each theme as a distribution of words. By uncovering these hidden topical patterns, researchers moved beyond simple sentiment classification – positive, negative, or neutral – to reveal what specifically drove public opinion during the 2024 Bangladesh Uprising. The algorithm effectively sifted through the vast dataset, grouping related terms and assigning each document a probability distribution across the identified themes, ultimately providing a nuanced understanding of the key issues resonating with the public. The resulting topic distributions offered a quantifiable and interpretable representation of the dominant narratives present in the online discourse.
The application of topic modeling to the Bangla Political Sentiment Dataset unearthed the central issues dominating public discourse during the 2024 Bangladesh Uprising. Analyses revealed that narratives surrounding economic hardship, governmental accountability, and calls for political reform were particularly prevalent. Beyond simply identifying keywords, the modeling process exposed the complex interplay between these themes, showing how anxieties about rising living costs were frequently linked to criticisms of political leadership and demands for systemic change. This suggests that public sentiment wasn’t driven by isolated concerns, but rather by a confluence of interconnected grievances that collectively fueled the uprising. The resulting thematic landscape offers valuable insight into the underlying motivations and emotional currents driving the socio-political events.
The convergence of topic modeling and sentiment analysis offers a robust methodology for dissecting intricate socio-political phenomena. By first identifying prevalent themes within a corpus – such as the key issues driving public discourse – and then gauging the emotional tone associated with each theme, researchers can move beyond simple keyword counts to grasp the nuances of public opinion. This approach, applied to the Bangla Political Sentiment Dataset, yielded particularly insightful results regarding the 2024 Bangladesh Uprising. Crucially, the validity of the thematic interpretations was reinforced by high inter-annotator reliability, as evidenced by a Cohen’s Kappa score of 0.78, demonstrating substantial agreement among coders and bolstering confidence in the identified narratives shaping public sentiment.
The analysis detailed within this study demonstrates how seemingly disparate data points – Facebook headlines in this instance – coalesce to reveal a nation’s emotional state. This echoes Andrey Kolmogorov’s assertion: “Probability theory is nothing but the science of logical consistency.”. The rigorous application of sentiment analysis and LDA topic modeling isn’t merely about identifying keywords; it’s about establishing a logically consistent framework for understanding the underlying emotional responses-outrage, hope, despair-that define a period of intense political and social upheaval. Documentation captures structure, but behavior emerges through interaction, and this study exemplifies how analyzing those interactions illuminates collective sentiment.
The Road Ahead
The analysis of public sentiment during periods of crisis, as demonstrated by this work with Bangla headlines, reveals a crucial truth: understanding the emotional landscape is not simply about cataloging feelings. It’s about tracing the flow of information – the tributaries feeding the river of public opinion. Attempting to ‘fix’ a single emotive response without mapping the underlying network of narratives is akin to treating a symptom while ignoring the disease. The current models, while effective at identifying broad emotional categories, remain largely opaque regarding the why behind the outrage, hope, or despair.
Future research must move beyond surface-level detection. The next step isn’t simply about achieving higher accuracy in sentiment classification; it’s about developing models that can dissect the structure of sentiment. How do specific linguistic features – metaphor, framing, appeals to authority – contribute to the formation of collective emotion? The dataset presented here offers a valuable foundation, but the true challenge lies in building systems capable of identifying the causal links between language, context, and emotional response.
Ultimately, the goal isn’t to predict sentiment, but to understand the architecture of collective feeling. One cannot simply replace the ‘heart’ of public opinion without understanding the entire ‘bloodstream’ of information and narrative that sustains it. A deeper understanding of this structure will not only refine sentiment analysis techniques but also offer critical insights into the dynamics of social and political upheaval.
Original article: https://arxiv.org/pdf/2512.15547.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-18 21:37