Twitter Sentiment Analysis Using NLP Models and Real-Time Tweet Fetching Article Swipe
Bheemalingappa
,
Chandrashekar K L
,
Darshan S
,
Dattatreya
,
Asstient Professor Sumitra Sharma Phurailatpam
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17797433
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17797433
Social media platforms, particularly Twit- ter, generate massive volumes of real-time textual data that reflect public opinion, emotional tendencies, and emerging societal trends. Analyzing this stream of infor- mation manually is infeasible due to its speed, scale, and linguistic complexity. This paper presents an enhanced real-time Twitter Sentiment Analysis system that inte- grates Natural Language Processing (NLP) models with live tweet fetching using the Twitter API v2. The pro- posed system employs a hybrid pipeline consisting of the VADER rule-based sentiment analyser for fast polarity detection and Transformer-based models for deeper con- textual sentiment classification. Additionally, the system incorporates optional modules for emotion recognition and toxicity analysis, enabling multi-dimensional inter- pretation of user-generated content. A Streamlit-based in- teractive interface allows users to fetch tweets in real time, analyze sentiment distributions, examine top key- words, and download processed outputs. The architec- ture is designed for scalability, efficiency, and accessibil- ity, offering a low-cost yet powerful solution for social sentiment monitoring and data-driven decision-making. Experimental evaluations demonstrate that the model combination improves interpretability and accuracy while maintaining responsiveness suitable for real-time applications.
Related Topics
Concepts
Sentiment analysis
Interpretability
Computer science
Pipeline (software)
Social media
Artificial intelligence
Natural language processing
Natural language
Analyser
Black box
Semi automatic
Machine learning
Language model
Deep learning
Information retrieval
Big data
Polarity (international relations)
Event (particle physics)
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17797433
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7108473061
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W7108473061Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17797433Digital Object Identifier
- Title
-
Twitter Sentiment Analysis Using NLP Models and Real-Time Tweet FetchingWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-03Full publication date if available
- Authors
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Bheemalingappa, Chandrashekar K L, Darshan S, Dattatreya, Asstient Professor Sumitra Sharma PhurailatpamList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17797433Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17797433Direct OA link when available
- Concepts
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Sentiment analysis, Interpretability, Computer science, Pipeline (software), Social media, Artificial intelligence, Natural language processing, Natural language, Analyser, Black box, Semi automatic, Machine learning, Language model, Deep learning, Information retrieval, Big data, Polarity (international relations), Event (particle physics)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.toxicity | 105 |
| abstract_inverted_index.Analyzing | 23 |
| abstract_inverted_index.Sentiment | 47 |
| abstract_inverted_index.analysis, | 106 |
| abstract_inverted_index.architec- | 139 |
| abstract_inverted_index.detection | 85 |
| abstract_inverted_index.emotional | 17 |
| abstract_inverted_index.interface | 118 |
| abstract_inverted_index.pretation | 110 |
| abstract_inverted_index.processed | 136 |
| abstract_inverted_index.real-time | 10, 45, 178 |
| abstract_inverted_index.sentiment | 80, 93, 128, 157 |
| abstract_inverted_index.teractive | 117 |
| abstract_inverted_index.Processing | 55 |
| abstract_inverted_index.consisting | 75 |
| abstract_inverted_index.infeasible | 31 |
| abstract_inverted_index.linguistic | 38 |
| abstract_inverted_index.monitoring | 158 |
| abstract_inverted_index.platforms, | 2 |
| abstract_inverted_index.rule-based | 79 |
| abstract_inverted_index.accessibil- | 147 |
| abstract_inverted_index.combination | 168 |
| abstract_inverted_index.complexity. | 39 |
| abstract_inverted_index.data-driven | 160 |
| abstract_inverted_index.demonstrate | 164 |
| abstract_inverted_index.efficiency, | 145 |
| abstract_inverted_index.evaluations | 163 |
| abstract_inverted_index.maintaining | 174 |
| abstract_inverted_index.recognition | 103 |
| abstract_inverted_index.tendencies, | 18 |
| abstract_inverted_index.Experimental | 162 |
| abstract_inverted_index.incorporates | 98 |
| abstract_inverted_index.particularly | 3 |
| abstract_inverted_index.scalability, | 144 |
| abstract_inverted_index.Additionally, | 95 |
| abstract_inverted_index.applications. | 179 |
| abstract_inverted_index.distributions, | 129 |
| abstract_inverted_index.responsiveness | 175 |
| abstract_inverted_index.user-generated | 112 |
| abstract_inverted_index.Streamlit-based | 115 |
| abstract_inverted_index.classification. | 94 |
| abstract_inverted_index.decision-making. | 161 |
| abstract_inverted_index.interpretability | 170 |
| abstract_inverted_index.Transformer-based | 87 |
| abstract_inverted_index.multi-dimensional | 108 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |