hotel review sentiment analysis mtech thesis report
Budget: ₹12,500 – ₹37,500 INR
I’m compiling a master-level thesis that zeroes in on sentiment analysis of online hotel reviews. The core mission is to map the current state-of-the-art, pinpoint where scholars and practitioners still fall short, and then outline a solid path forward. Two gaps have my full attention:
• Dataset limitations – especially the uneven quality of publicly available corpora (noise, labeling inconsistencies, sparse multilingual coverage).
• Model performance – why promising architectures (e.g., BERT, RoBERTa, hybrid CNN-BiLSTM) still fail on nuanced hospitality-specific language such as sarcasm, service jargon, or culturally driven idioms.
Your task is to craft a literature review that draws these threads together, supports each claim with up-to-date citations, and ends with a defensible research agenda. Python/NLP tool familiarity (spaCy, scikit-learn, TensorFlow) will help you critique prior work convincingly and sketch the proposed experimental setup.
Deliverables
1. Narrative literature review (≈6,000–8,000 words) highlighting trends, conflicts, and consensus in hotel review sentiment studies.
2. Structured gap analysis matrix explicitly flagging dataset quality issues and model performance shortfalls.
3. Proposal for a new or augmented dataset: collection plan, data schema, ethical considerations, quality-assurance steps.
4. Conceptual model and methodology section ready for a thesis chapter, detailing algorithms, evaluation metrics, and anticipated improvements over baselines.
5. Compiled thesis-style report (Word or LaTeX) incorporating all sections, figures, and references in APA 7th.
Acceptance criteria
• Every gap is backed by at least three peer-reviewed sources from 2019-2024.
• Dataset proposal addresses noise reduction, balanced class distribution, and cultural diversity.
• Methodology section specifies train/validation/test split strategy and reproducible evaluation metrics (F1, AUC, RMSE where applicable).
• Turnitin similarity below 10 %.
Timeline and communication cadence are flexible; however, a first draft of the review and gap matrix within two weeks would keep the thesis schedule on track.
• Dataset limitations – especially the uneven quality of publicly available corpora (noise, labeling inconsistencies, sparse multilingual coverage).
• Model performance – why promising architectures (e.g., BERT, RoBERTa, hybrid CNN-BiLSTM) still fail on nuanced hospitality-specific language such as sarcasm, service jargon, or culturally driven idioms.
Your task is to craft a literature review that draws these threads together, supports each claim with up-to-date citations, and ends with a defensible research agenda. Python/NLP tool familiarity (spaCy, scikit-learn, TensorFlow) will help you critique prior work convincingly and sketch the proposed experimental setup.
Deliverables
1. Narrative literature review (≈6,000–8,000 words) highlighting trends, conflicts, and consensus in hotel review sentiment studies.
2. Structured gap analysis matrix explicitly flagging dataset quality issues and model performance shortfalls.
3. Proposal for a new or augmented dataset: collection plan, data schema, ethical considerations, quality-assurance steps.
4. Conceptual model and methodology section ready for a thesis chapter, detailing algorithms, evaluation metrics, and anticipated improvements over baselines.
5. Compiled thesis-style report (Word or LaTeX) incorporating all sections, figures, and references in APA 7th.
Acceptance criteria
• Every gap is backed by at least three peer-reviewed sources from 2019-2024.
• Dataset proposal addresses noise reduction, balanced class distribution, and cultural diversity.
• Methodology section specifies train/validation/test split strategy and reproducible evaluation metrics (F1, AUC, RMSE where applicable).
• Turnitin similarity below 10 %.
Timeline and communication cadence are flexible; however, a first draft of the review and gap matrix within two weeks would keep the thesis schedule on track.