College it project batch number 39.pptx

It's a data science project

1. RAGHU INSTITUTE OF TECHNOLOGY (AUTONOMOUS) (Approved by AICTE, Affiliated to JNTUK, Accredited by NBA & NAAC) Department of Computer Science and Engineering DAKAMARRI, VISAKHAPATNAM A Project Report by: 213J1A05E0: PODURU CHAITANYA SAI 213J1A05F1: ROKKAM ASHRITHA 213J1A05H6: V LAV 213J1A05I3: VASIPALLI NITHISH Project Guide: DR. S. OM PRAKASH Associate Professor Department Of CSE

2.   Tourism and Restaurant Recommendation System based on customer reviews through Sentiment Analysis. Project No. 39

3. INDEX Abstract Introduction Objectives Methodology Literature Survey Project Flow References

4. Abstract This project develops a novel recommendation system that leverages sentiment analysis of customer reviews to suggest personalized tourism destinations and restaurants. The Dataset for this project is derived from various websites through which customers book their travelling or through which customers book their slot for Exquisite Restaurant Cuisine. The system utilizes natural language processing (NLP) techniques and Machine Learning Algorithms to extract sentiment scores, which are then used to rank and recommend destinations and restaurants based on user preferences. The system provides personalized recommendations, improves accuracy through sentiment analysis, enhances user experience, and offers valuable insights for businesses. Along with visualising its results carefully, one of its tasks will be to improve accuracy. This project contributes a new approach to tourism and restaurant recommendation, a comprehensive evaluation framework, and a scalable system for various applications.

5. Introduction Developing a recommendation system for tourism and restaurants by analyzing 10,000 restaurant reviews sourced from Kaggle and additional manually curated tourism reviews. Compares traditional lexicon-based sentiment analysis with advanced transformer architectures to provide insights into customer sentiments and enhance decision-making. Final Outcome will be that, Users receive tailored recommendations for restaurants or tourist destinations based on their preferences and sentiment analysis results. Objectives: To Develop a recommendation system using sentiment analysis on customer reviews. Compare traditional lexicon-based methods with sophisticated transformer architectures. Demonstrate the use of Hugging Face pipelines for practical sentiment analysis. Identify strengths and weaknesses of the models in interpreting complex sentiments. Provide sentiment scores based on customer feedback.

6. Methodology Data Collection: Gather 10,000 restaurant and additional tourism reviews. Data Exploration: Perform exploratory data analysis (EDA) to understand sentiment patterns. Model Implementation: Apply a traditional lexicon-based approach for initial scoring. Implement an advanced transformer architecture using Hugging Face. Model Comparison: Using accuracy, precision and F1-score for evaluation. Error Analysis: Analyse misclassification instances to refine models. Scalability: Demonstrate Hugging Face's pipelines for efficient sentiment analysis. Societal Impact: This recommendation system can enhance consumer experiences in tourism and dining by providing personalized suggestions based on customer sentiments. By leveraging insights from reviews, businesses can improve their services and foster customer loyalty, ultimately contributing to community economic growth.

7. Literature Survey Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering. Ranking Tourist Attractions through Online Reviews: A Novel Method with Intuitionistic and Hesitant Fuzzy Information Based on Sentiment Analysis. Analyzing tourism reviews using an LDA topic-based sentiment analysis approach . Knowledge based topic retrieval for recommendations and tourism promotions. Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes. Improving the accuracy of sentiment analysis using a linguistic rule-based feature selection method in tourism reviews.

8. Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering(1). Authors : Choirul Huda, Yaya Heryadi , Lukas, and Widodo Budiharto Date : September 2024 | Organisation : IEEE Contributions : Hybrid Recommender Model : A hybrid approach combining User-Based Collaborative Filtering (UBCF), Demographic Filtering (DF), Aspect-Based Sentiment Analysis (ABSA), and Content-Boosted Collaborative Filtering (CBCF). Cold-Start Problem Solution : Addressed the cold-start issue by incorporating demographic and sentiment data into collaborative filtering. Performance Improvement : Enhanced recommendation accuracy by reducing user-item matrix sparsity with synthetic data generation. Dataset Creation : Developed a detailed tourism dataset from TripAdvisor reviews, supplemented with Google Maps data for future research.

9. Challenges : Sparse Matrix Problem : Limited user ratings caused sparsity in the user-item matrix, leading to inaccurate recommendations. Cold-Start Problem : Difficulty in recommending items to new users with no prior ratings or interactions. Data Integration : Challenges in merging qualitative user review data into quantitative models for more accurate predictions. Proposed Solutions : Content-Boosted Collaborative Filtering (CBCF) : Reduced sparsity by generating synthetic ratings using demographic data and sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) : Converted review sentiments into numerical ratings, improving matrix density for better predictions. Demographic Filtering (DF) : Integrated demographic data to enhance recommendations, particularly for users with little interaction, solving the cold-start issue.

10. Results : Significant Performance Improvement : Notable improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) compared to traditional UBCF. CBCF models improved MAE by 84.7% and RMSE by 82.3%. Dense UI-Matrix : The hybrid model increased user-item matrix density from 38% to 100%, leading to more accurate recommendations. Effective Cold-Start Solution : CBCF, along with ABSA and DF, successfully resolved the cold-start problem by generating synthetic ratings using sentiment analysis and demographic data. Overall, the study shows that combining multiple recommendation techniques significantly boosts accuracy and enhances user satisfaction in smart tourism applications.

11. Ranking Tourist Attractions through Online Reviews: A Novel Method with Intuitionistic and Hesitant Fuzzy Information Based on Sentiment Analysis(2). Authors : Yong Qin, Xinxin Wang, Zeshui Xu Date : March 2021 | Organization : Springer Contributions : Aspect-Level Sentiment Analysis : Introduced a method for evaluating tourist attractions (TAs) based on detailed aspect-level sentiment analysis of online reviews, offering more precision than document or sentence-level approaches. Three-Level Evaluation System : Developed a system with three layers: target, criteria, and sub-criteria, to ensure a thorough evaluation of TAs. IHF-TOPSIS Method : Proposed a new ranking method using intuitionistic and hesitant fuzzy sets (IFS and HFS) to handle ambiguity in reviews. This method combines feedback from both potential and experienced tourists to rank TAs.

12. Use of Fuzzy Sets : The study creatively integrates Intuitionistic Fuzzy Sets (IFS) and Hesitant Fuzzy Sets (HFS) to better address uncertainty in tourist reviews. Challenges : Information Overload : Processing the vast amount of online reviews poses a significant challenge for tourists when choosing destinations. Ambiguity and Uncertainty : Reviews often contain unclear or hesitant information, making sentiment-based rankings difficult. Subjectivity in Preferences : Potential and experienced tourists may have differing preferences, leading to varying evaluations of tourist attractions. Proposed Solutions : Aspect-Level Sentiment Analysis : This method pinpoints key aspects of reviews (e.g., service, scenery) and their sentiment (positive, negative, neutral), making it easier to handle large datasets and improve review analysis.

13. Fuzzy Set Theory (IFS and HFS) : These fuzzy sets were used to handle ambiguity and hesitation in reviews, capturing varying levels of satisfaction and dissatisfaction. IHF-TOPSIS Method : A multi-criteria decision-making (MCDM) approach that combines sentiment analysis with fuzzy logic, enabling more precise and nuanced rankings of tourist attractions. Results : Improved Ranking Accuracy : The IHF-TOPSIS method enhanced the ranking accuracy of tourist attractions by incorporating both positive and hesitant fuzzy data, better reflecting tourist preferences. Comprehensive Feedback : The 3-level evaluation system provided in-depth analysis, aiding tourists in decision-making and helping service providers improve their offerings based on sentiment feedback. Practical Application : A case study validated the method’s effectiveness, demonstrating its real-world applicability for ranking tourist attractions based on online reviews.

14. Analyzing tourism reviews using an LDA topic-based sentiment analysis approach(3). Authors : Twil Ali, Bencharef Omar, Kaloun Soulaimane Date : November 2022 | Organization : Elsevier Contributions : Novel Integration : The authors introduced a method that combines Latent Dirichlet Allocation (LDA) with sentiment analysis to extract and analyze tourism-related topics from TripAdvisor reviews. This approach provides tourism practitioners with deeper insights into tourist opinions and experiences. Aspect-Based Sentiment Analysis : By integrating LDA for topic modeling with sentiment analysis, user feedback is categorized by specific topics and sentiment, revealing the strengths and weaknesses of tourist attractions. Case Study on Marrakech : The method was applied to over 39,200 English reviews from TripAdvisor for Marrakech, demonstrating its practical applicability.

15. Challenges : Complexity in Understanding Tourist Sentiments : Human language complexity, including irony, sarcasm, and cultural differences, can affect the accuracy of sentiment analysis algorithms. Destination-Specific Data : Results from this study may not be generalizable to other tourist destinations due to the specific nature of the data collected from Marrakech reviews. Language Limitation : The focus on English reviews restricts the capture of sentiments expressed in other languages, which is particularly limiting for a multicultural city like Marrakech. Proposed Solutions : Web Scraping for Data Collection : A Python-based Selenium script was utilized to scrape TripAdvisor reviews, ensuring a comprehensive dataset for analysis. Data Preprocessing : Reviews underwent preprocessing using natural language processing (NLP) techniques, including the removal of stop words, punctuation, irrelevant words, and performing stemming and lemmatization to prepare the text for analysis. Topic Modeling with LDA : The LDA algorithm was applied to extract latent topics from the reviews, which were labeled by tourism experts to highlight key aspects of tourist experiences, such as the Jamaâ-el-Fna atmosphere and shopping experiences.

16. Lexicon-Based Sentiment Analysis : Sentiment analysis was performed using VADER and TextBlob , two well-known lexicon-based algorithms, to assess the sentiment of reviews associated with each topic. Results : Identified Topics : Four key topics were identified: Jamaâ-el-Fna atmosphere, shopping experience, citizen behavior, and overall touristic experience. Sentiment Scores : Reviews were classified as positive, neutral, or negative, revealing a strong correlation between sentiment scores from the algorithms and the TripAdvisor bubble rating system. Most reviews indicated a positive sentiment overall. Model Accuracy : The sentiment analysis models were benchmarked, with TextBlob achieving 77.3% accuracy, VADER at 72.6%, and JST at 69.6%. Insights for Tourism Practitioners : The study offered actionable insights into tourist feedback, emphasizing areas needing improvement, such as pricing and local behavior. Overall, this approach serves as a valuable framework for analyzing tourist feedback, though the limitations of rule-based sentiment analysis models and the dataset's specificity highlight areas for further enhancement.

17. Knowledge based topic retrieval for recommendations and tourism promotions (4) Authors : Ram Krishn Mishra, J Angel Arul Jothi, Siddhaling Urolagin , Kayan Irani Date : December 2023 | Organisation : Elsevier Contributions : Tourism and Recommender Systems : The paper introduces a system using online reviews to recommend restaurants based on tourists' interests. Automated Rating Prediction : It predicts restaurant ratings from user reviews using Random Forest and Decision Tree classifiers, with high accuracy. Feedback Model : A model using salience and valence from topics like food and service to provide insights on restaurant performance. Knowledge-Based System : The system uses topic modeling to identify key themes from reviews and recommends restaurants accordingly. Challenges : Handling Large Review Datasets : Extracting useful insights from vast online reviews for both users and managers.

18. Predicting Ratings from Reviews : Matching user ratings with the sentiment in their reviews is difficult. Topic Modeling : Identifying important topics like service or food quality from reviews is challenging. Proposed Solutions : Machine Learning Algorithms : The system applies machine learning techniques (Random Forest, Decision Tree, Support Vector Machine (SVM)) to predict star ratings from text reviews. Clustering and Topic Modeling : K-means clustering, Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF) are used to group reviews into topics, identifying what users are commenting on (e.g., food quality, service). Feedback Model Based on Salience and Valence : The system uses salience and valence calculations to create feedback models for restaurants, aiding business improvement decisions.

19. Results : High Accuracy in Rating Predictions : Random Forest and Decision Tree classifiers achieved top accuracy, 87.59% and 84.93%, respectively, in predicting ratings from reviews. Effective Feedback Generation : The feedback model delivered key insights on restaurant performance across various topics, offering actionable steps for improving customer experience. Personalized Recommendations : The knowledge-based system successfully generated personalized recommendations based on users' preferences for specific topics like food and service time. These results highlight the effective use of machine learning in improving tourism recommendations and restaurant discovery while offering insights for business improvements.

20. Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes (5) Authors : XIAO ZHOU, MINGZHAN SU, GUANGHUI FENG, AND XINGHAN ZHOU Date : January 2021 | Organisation : IEEE Contributions : Tourist Interest and Route Recommendation : The paper presents a tourism recommendation algorithm using text mining and the MP (McCulloch-Pitts) nerve cell model to match tourist sights with interests while considering transportation modes. Feature-Based Tourist Clustering : A sight clustering algorithm based on feature attributes and interest labels is developed for more accurate sight recommendations. Multimodal Transportation in Tourism : The research proposes a tour route chain algorithm that integrates transportation modes, maximizing satisfaction by considering factors like convenience and travel time. Challenges : Tourist Interest Mining : Extracting preferences from large, unstructured datasets is difficult.

21. Tour Route Planning : Integrating real-world geographic and traffic data with tourist preferences to create optimal routes for different transport modes is complex. Over-dependence on Simulations : Many algorithms rely too much on simulations, failing to consider real-world factors like geography and traffic. Proposed Solutions : Text Mining and Clustering : The algorithm uses text mining to analyze large datasets, matching tourist sites with individual preferences based on extracted interests. Multivariate Transportation Model : The MP nerve cell model simulates the impact of various transportation modes (e.g., walking, bus, car) on tourist route selection and satisfaction, combining geographic, traffic, and tourist interest data for optimal route creation. Iterative Nerve Cell Algorithm : The model iteratively refines route choices by considering multiple layers of influence, including geographic and transportation factors.

22. Results: Improved Tourist Satisfaction : The developed algorithm outperforms traditional methods by providing more personalized and efficient route recommendations. It results in higher satisfaction by better matching tourist interests and transportation modes. Feasibility and Practicality : The experimental tests show that the algorithm is feasible and practical, offering advantages in motive satisfaction and reducing both time and space complexity compared to baseline methods. Real-World Application : The algorithm successfully integrates real-world data, such as geographic and transportation information, providing an effective tool for both tourists and tourism administrations to improve service offerings and optimize urban transportation strategies Overall, This approach offers a more realistic and user-focused recommendation system for tourism, emphasizing efficiency and satisfaction in real-world travel scenarios.

23. Improving the accuracy of sentiment analysis using a linguistic rule-based feature selection method in tourism reviews (6) Authors : N. Saraswathi, T. Sasi Rooba , S. Chakaravarthi Date : August 2023 | Organization : Elsevier Contributions: Linguistic Rule-Based Feature Selection : Introduces a new method for feature selection based on linguistic rules. It extracts features like parts of speech (POS) tags and n-grams (unigrams, bigrams, trigrams) and applies statistical filters (Information Gain, Chi-Square, Gini Index) to choose the most relevant ones. Improved Sentiment Classification : Uses an ensemble learning model (Random Forest, Naive Bayes, and Support Vector Machines) to enhance sentiment classification in tourism reviews. Handling High Dimensionality : Tackles the challenge of high-dimensional data by applying feature ranking techniques to remove irrelevant features and improve model accuracy.

24. Challenges: Noisy Data and Irrelevant Features : Unstructured user reviews (UUR) often include noisy or irrelevant data, making sentiment analysis harder. High-dimensional data further reduces model performance. Limitations of Traditional Sentiment Analysis : Methods like Bag-of-Words and basic n-grams often miss semantic meaning and word order, limiting their effectiveness with large, complex datasets. Proposed Solutions: Linguistic Rule-Based Approach : A rule-based method is proposed to filter out non-sentiment features by applying linguistic rules. It prioritizes keywords like "but," "however," and "despite" to capture sentiment more effectively. Statistical Filtering for Feature Selection : Statistical filters (Information Gain, Chi-Square, Gini Index) are used to rank and select key features for sentiment classification.

25. Ensemble Learning Model : Combines classifiers (Random Forest, Naive Bayes, SVM) to improve accuracy. The Majority Voting Threshold (MVT) helps refine feature selection for better classification performance. Results: Improved Accuracy : The proposed method outperformed baseline models in sentiment classification, achieving 94.7% accuracy. This shows the effectiveness of combining linguistic rules, feature selection, and ensemble learning. Comparison with Baseline Methods : The linguistic rule-based approach enhanced sentiment prediction, especially for complex unstructured reviews in tourism, compared to traditional methods. Performance Analysis : Metrics like accuracy, precision, recall, and F-measure across datasets prove that intelligent feature selection significantly boosts classification performance. Overall, The combination of linguistic rules and statistical filtering effectively addresses challenges related to noisy and high-dimensional data in sentiment analysis.

26. Project Flow Data Collection: Collect reviews from Kaggle and other sources. Data Preprocessing : Clean the dataset of duplicates and missing values. Exploratory Data Analysis (EDA): Visualize sentiment distributions and trends. Modeling: Implement lexicon-based and transformer models for sentiment analysis. Evaluation: Compare methods using standard metrics and analyze misclassifications. Insights & Conclusion: Summarize findings and their implications for recommendations. Outcome: Finally, the User will receive tailored recommendation for restaurant or tourist destination based on their preferences and sentiment analysis results.

27. References Keval Pipalia , Rahul Bhadja and Madhu Shukla . " Comparative Analysis of Different Transformer Based Architectures Used in Sentiment Analysis”, I EEE –2020 ISBN: 978-1-7281-8908-6 ( 2020 ) . ALIREZA POURKEYVAN, RAMIN SAFA, AND ALI SOROURKHAH . " Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks”, I EEE Access DOI 10.1109/ACCESS.2024.3366653 ( 2024 ). XI SHAO, GUIJIN TANG, AND BING-KUN BAO . " Personalized Travel Recommendation Based on Sentiment-Aware Multimodal Topic Model ”, I EEE Access DOI 10.1109/ACCESS.2019.2935155 ( 2019 ).

28. Mahmud Isnana , Gregorius Natanael Elwirehardja , Bens Pardamean . " Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model ”, Elsevier 10.1016/j.procs.2023.10.514 ( 2023 ). Loukas Samaras, Elena García- Barriocanal , Miguel-Angel Sicilia . " Sentiment analysis of COVID-19 cases in Greece using Twitter data ”, Elsevier - doi.org/10.1016/j.eswa.2023.120577 ( 2023 ). Odeyinka Abiola1, Adebayo Abayomi‑Alli, Oluwasefunmi Arogundade Tale, Sanjay Misra and Olusola Abayomi‑Alli . " Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyzer”, Springer - doi.org/10.1186/s43067-023-00070-9 ( 2023 ). Nyein Nyein Myo, Khin Zezawar Aung . " Sentiment Analysis of Students’ Comment Using Lexicon Based Approach ”, IEEE ICIS - 978-1-5090-5507-4/17 ( 2017 ).

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