Volume 15, Issue 6

Public Sentiment Analysis of India's Mangalyaan Mars Mission Using Multimodal Social Media Data: A Systematic Literature Review

Author

Irfan Y. Belim¹, Dr. Bhargav Rajyagor²

Abstract

India's Mangalyaan Mars Orbiter Mission (MOM), launched in November 2013 and operational until September 2022, generated substantial public discourse across social media platforms including YouTube, Twitter/X and Reddit   making it one of the most socially engaged interplanetary missions in history. Systematic extraction and analysis of this discourse offer valuable insights into public perception of low-cost space exploration, national scientific pride and science communication effectiveness. This paper presents a systematic literature review of 31 peer-reviewed publications to construct a unified 32-parameter multimodal framework for analyzing public sentiment expressed on social media in the context of the Mangalyaan mission. The 32 parameters span seven analytical categories: Text Analysis (speech-to-text extraction, subtitle sentiment, keyword polarity, emotion classification, negation and sarcasm detection, comment sentiment and emoji/hashtag sentiment), Audio Analysis (tone positivity/negativity, pitch and speech rate, emotional prosody and crowd reaction), Visual Analysis (facial expression recognition, body language, mission control reactions, event detection and scene color sentiment), Metadata Analysis (title/description sentiment, engagement metrics, upload timing and trending indicators), Mission-Specific Context (orbit insertion confirmation, telemetry loss references, official statement extraction and media sentiment comparison), Temporal Analysis (pre/during/post mission sentiment, trend over time and viral velocity) and Output parameters (overall classification, multimodal score, confidence score and visualization). Findings confirm that transformer-based models achieve F1 scores above 0.90 and multimodal fusion outperforms single-modality approaches by 8–15%. Critical gaps include the absence of Mangalyaan-specific annotated datasets, limited code-mixed Hinglish sentiment capability and underdeveloped cross-platform sentiment fusion for space mission discourse.

Keywords: Mangalyaan; Mars Orbiter Mission; Public Sentiment Analysis; Social Media Mining; Multimodal NLP; Emotion Classification; Space Science Communication; ISRO

DOI Link: https://doi.org/10.62226/ijarst20262702

Google Scholar: https://scholar.google.com/Public-Sentiment-Analysis-of-Indias-Mangalyaan-Mars-Mission-Using-Multimodal-Social-Media-Data-A-Systematic-Literature-Review

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DOI

https://doi.org/10.62226/ijarst20262702

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