Gather & Analyze Customer Reviews Online

@afua23

Hello there, I have read your project with title Gather & Analyze Customer Reviews Online carefully and i can help you. I have a 15+ experience in web scraping. Here are a few examples of past projects i have completed here: https://www.freelancer.com/projects/data-entry/Scrape-Data-from-Websites-39311571/details https://www.freelancer.com/projects/data-extraction/Web-Database-Extraction-Script/details https://www.freelancer.com/projects/php/PYTHON-SCRAPER-35879443/details https://www.freelancer.com/projects/excel/Web-scraping-for-public-information/details Let's chat so that we can discuss the fine details/ Thanks Claudia

@ShaikhAneesa

Hello, I have thoroughly analyzed the project requirements for web scraping and review sentiment analysis. I understand the need to gather a minimum of 500 customer reviews from various online platforms like Google Reviews, Reddit, TripAdvisor, Instagram, YouTube, TikTok, and relevant forums. Let's chat and discuss it further. To handle your project, I will start with developing custom web scrapers using Python and BeautifulSoup for data extraction. I will then utilize NLTK for sentiment analysis and classification. The final output will include two Excel files as specified. The deliverables for this project will be a review-level dataset and a topic-level dataset, both accurately classified and organized. Before signing-off my bid, I would like to ask a question, i.e., what specific categories and subcategories should be considered for topic classification? Best Regards, Aneesa.

@TeraByte26

Hi Sandra, I will deliver at least 500 public reviews from Google, Reddit, TripAdvisor, Instagram, YouTube, TikTok, X and forums on 25 Sept 2026, with fields source, location, rating, text, date, reviewer, language, owner_response, URL, plus two Excel files (review‑level and topic‑level) classified by sentiment. I will complete both Excel files within three days of the scrape for €200. Sample ready. Thanks. Waiting for your response in chat! Best Regards.

@AndrewJSMAN

I've built web scraping pipelines for review aggregation before, so this is right in my wheelhouse. I'll use Python with Scrapy and BeautifulSoup for extraction, then clean and deduplicate with pandas. For sentiment analysis, I'll leverage spaCy for topic modeling and custom rules for classification. I'll scrape Google Reviews, TripAdvisor, and Reddit first, then expand to TikTok/X if needed. Delivery will be 25 September 2026, with validation checks for consistency. I can outline the exact workflow after reviewing the target URLs. Thanks, Andrii.

@denisSolution

Hi, I'm Denis, a full-stack developer specializing in data collection pipelines. I've built similar projects involving web scraping, sentiment analysis, and structured data export. I understand your needs: gather customer reviews from multiple sources, extract key fields, and deliver two datasets—one with review-level sentiment classification, and another breaking reviews down by topic. **My approach:** I'll assess target sources for scrapability, build extraction scripts for each platform, normalize data into a consistent schema, deduplicate, and run sentiment analysis. Finally, I'll generate Excel outputs with proper categorization and topic extraction. **Technical highlights:** I'll combine rule-based classification with a lightweight language model for nuanced sentiment analysis that distinguishes mixed sentiment from neutral reviews. For topic extraction, I'll identify key phrases to keep data meaningful without over-splitting. **Realistic challenges:** Rate limits and language variation across platforms may slightly affect accuracy. I'll handle both through careful request management and validation sampling. **Scope & Timeline:** I can gather 500+ reviews across Google Reviews, TripAdvisor, Reddit, and YouTube (approximately 150-200, 100-150, 100-150, and 50-100 respectively). Delivery: 5-7 days. Cost: $400-600 USD depending on source complexity and data volume. I'm ready to start immediately. Let's discuss the details. Thanks, Denis.

@eshA0010

As an Excel expert, data analyst, and enthusiastic problem-solver, I'm a perfect fit for your web scraping and sentiment analysis project. I have in-depth knowledge and hands-on experience in all the areas required for successful completion. From web scraping to extracting, cleaning, analyzing, and compiling large datasets in Excel, my specialization aligns well with your requirements. I have amassed immense proficiency in Excel data management, VBA Macros, web scraping using Python which enables me to transform scattered raw data into organized insights. My track record showcases my ability to process massive amounts of information without compromising accuracy or cleaning quality diagonally diverse sources. I not only bring technical know-how but also exceptional attention to detail ensuring any duplicates are removed while maintaining data reliability. I prioritize clean formatting, organized datasets and reliable results which is paramount in your project. What more? I guarantee rapid turnarounds without any compromise on quality. Overall, my extensive skillset along with efficient project management ensures that I can capably deliver the high volume thorough analysis you require on time and within budget. Choose me for a reliable yet cost-effective approach that will furnish you with actionable insights from your customer reviews!

@ImranAli01

Hi, I got that you are looking for a web scraping and data analysis specialist to gather a minimum of 500 customer reviews from various online platforms like Google Reviews, Reddit, TripAdvisor, Instagram, YouTube, TikTok, and relevant forums. This is what I can help you with, let's chat. My approach will be to utilize advanced web scraping tools to extract the required data efficiently. By applying sentiment analysis techniques using Python and Natural Language Processing, I will classify reviews into Positive, Neutral, or Negative sentiments. The final deliverables will include two Excel files: one with review-level data and another with topic-level data, ensuring accurate classification and easy interpretation of results. As final deliverables, you will receive two Excel files containing the review-level dataset and the topic-level dataset, organized according to your specifications. One thing I'd like to confirm before we start: Will the extraction need to be performed in real-time, or can historical data be utilized? I will ensure the collected data is clean, structured, accurately classified, and ready for analysis. I’d be glad to discuss the sources, data structure, and analysis requirements with you so we can align the workflow before execution. Looking forward to working together. Regards, Imran A.

@MSaadJaved

Hi, I’ll collect and structure 500+ public customer reviews from Google Reviews, Reddit, TripAdvisor, YouTube, TikTok, X, and relevant forums, delivering both a review-level Excel file and a topic-level Excel file with duplicate removal, source metadata, and sentiment labels. The main technical concern is preserving consistency across mixed platforms and handling posts with different formats/languages, so I’ll normalize fields, deduplicate carefully, and validate extracted owner responses and URLs. I recently built a multi-source review pipeline that mapped public comments into analysis-ready datasets with sentiment and topic tagging. For your project, I’d first confirm source availability and extraction rules for each platform, then run collection on the specified date and QA the final files for completeness and schema accuracy. Which sources are highest priority if some platforms return fewer publicly accessible reviews? Best regards, NexLoom

@C0RETECHIES

Hello, We understand you need a one‑time scrape of at least 500 customer reviews from Google Reviews, Reddit, TripAdvisor, Instagram, YouTube, TikTok, X and public forums on 25 Sep 2026, extracting source, location/listing, rating, review_text, date, reviewer, language, owner_response, review_url. The data must be deduplicated, sentiment‑classified (Positive/Neutral/Negative) and split into a review‑level Excel file and a topic‑level file where each review can appear up to four times with category, subcategory, topic, sentiment and intensity (1‑3). Technical approach: - Build Python adapters (Scrapy + Playwright) for each platform, scheduled for the target date. - Store raw records in a temporary PostgreSQL table, remove exact duplicates via a hash of review_url + review_text. - Apply a sentiment model (fine‑tuned DistilBERT) and a topic model (BERTopic/LDA) to derive category, subcategory, topic and intensity. - Export the two datasets as CSV then convert to Excel (xlsx) with proper column order. Core modules: 1. Source adapters - isolate platform‑specific pagination and rate‑limit handling. 2. Storage & deduplication - hash‑based dedup, DB indexing. 3. NLP pipeline - sentiment classification, topic modeling, evidence snippet extraction. 4. Export builder - creates review‑level and topic‑level rows, writes Excel files. Relevant systems: - Social Media Content Generator (n8n) - orchestrated multi‑source data collection and automated scheduling. - AI‑Powered Slack Clarification Assistant - used LangChain and transformer models for contextual understanding. - Automation Lead Generator - 8‑agent AI pipeline that performed web scraping, enrichment and CRM updates. Implementation strategy: Day 1‑2: Develop adapters and collect a 50‑review sample from each source. Day 3‑4: Train/validate sentiment and topic models on the sample; adjust intensity logic. Day 5‑6: Run full scrape, deduplicate, run NLP pipeline, generate draft Excel files. Day 7: QA against required fields, produce final files, prepare delivery. Questions: 1. Which category/subcategory taxonomy should we apply for topic labeling (or should we derive it dynamically)? 2. How should intensity (1‑3) be defined for each topic mention (e.g., based on keyword strength or sentiment confidence)? 3. Are there any access constraints (API keys, login requirements, rate limits) we need to respect for sources like Google Reviews or TikTok? Regards, Rohit

@qualityxenter

Affordable, Early Delivery. ★★★★★★★★★★★★★★I hold a Masters degree which gives me the requisite background to handle writing from various subjects. I am a highly committed person towards my work. You can rely on QualityXenter for quality and consistency in writing. We never violate copyright rules. I have vast amount of experience in this industry since I am working from 2015 as a professional writer. I provide many modifications till to get your satisfactions. I have access to enough journals to use in your research project. I always produce quality work at VERY LOW RATES so, don't worry if you have a low budget for your work, I will be very happy to make a new client like you. I am producing quality work for my clients including ARTICLE WRITING, REPORT WRITING, ESSAY WRITING, RESEARCH PAPERS, BUSINESS PLAN, TECHNICAL WRITING, MATLAB, THESIS, ACCOUNTING & FINANCE work ETC. Go through my profile link https://www.freelancer.com/u/qualityxenter

@robertb297

Extracting multi-channel customer reviews and breaking them down into granular topic-level sentiment records requires a consistent scraping pipeline and clear text classification. I can collect over 500 clean records across Google Reviews, TripAdvisor, Reddit, and YouTube, deduplicate the entries, and format the data into your two exact Excel templates with polarity and intensity scores. The final datasets will be structured and ready within two days of the September 25th run. Which specific brand listings, competitor links, or forum topics should be prioritized for this extraction? Happy to answer any questions you may have. Robert

@mahrsaad211

I can collect and analyze 500+ relevant public reviews from Google Reviews, Reddit, TripAdvisor, YouTube, X and relevant forums, with exact duplicates removed. I’ll deliver clean Excel datasets with all required fields, topic/category classification, sentiment, intensity, and evidence. Expected volume: 500–700+ reviews across suitable accessible sources. Delivery: 1–2 days after the collection date (25 September). Total cost: $50, including scraping, cleaning, deduplication, classification, and Excel formatting. Best Regards, Muhammad Saad

@ankits0192

Hi there, You need a clean review dataset where collection, deduplication, classification, and sentiment analysis are all traceable—not simply a scraped Excel file. I can collect publicly accessible reviews/comments on 25 September, normalize them into your required fields, remove exact duplicates, and produce both the review-level and topic-level Excel datasets. I can target 500+ relevant records across accessible sources such as Google Reviews, Reddit, TripAdvisor, YouTube, public forums, and other permitted public pages. The expected mix will depend on source accessibility and relevance, with source-level counts documented in the final dataset. Each review can be classified by category, subcategory, topic, sentiment, intensity (1–3), and supporting evidence. I’ll use Python/pandas for reproducible processing and provide the two Excel files with consistent IDs and validation. Estimated delivery: 2–3 days after collection. Proposed total: €150. 1. What business/listings, locations, and review topics should I target? 2. Should Instagram, TikTok, and X be included only where public content is accessible without login? Best regards, Ankit

@novatech2210

Hey, I just wrapped up a project scraping 650+ reviews from Google, TripAdvisor, Reddit, and YouTube, then turning the data into clean, sentiment‑classified Excel files. That’s exactly the system you need – no duplicates, every source field, and a topic‑level breakdown. I’ll pull the data on September 25 2026, targeting 500+ reviews: ~200 from Google, ~150 from TripAdvisor, ~100 from Reddit, ~50 from YouTube, and a few from Instagram/TikTok. Delivery will be 48 hours after scraping, with both review‑level and topic‑level sheets. Multiple 5‑star reviews on complex data‑collection and sentiment‑analysis projects. Let’s chat for a quick call; otherwise, I’ll send you a free consultation with preliminary insights. Chris | Lead Developer | Novatech

@lazarr4

Hello, I can scrape reviews from Google, Reddit, and TripAdvisor while filtering out duplicates and categorizing sentiment by topic. I’ve built a similar system for a travel agency—collected 600 reviews in two days, then used NLP to tag topics like "cleanliness" or "service" with 90% accuracy. I’d use Python with Scrapy and spaCy to extract structured data, then clean it with fuzzy matching to remove near-duplicates. This will let you track trends across platforms while keeping the data actionable. Better sentiment tracking will help you focus on high-impact reviews. I’ll provide a sample dataset before starting to confirm the approach. If this fits your needs, I can get the first batch ready by 25 September. Thanks, Lazar.

@copulas

As a dedicated team at COPULAS Co., Ltd., we bring a combined experience of over 15 years in full-stack engineering, with specific expertise in Web Scraping and Data Analysis, which is precisely what your project demands. We've successfully executed similar projects before, gathering customer reviews and performing sentiment analysis efficiently. Regarding the sources you need scraping from, we have experience with a wide array of platforms like Google Reviews, Reddit, TripAdvisor, Instagram, YouTube. Therefore, we are well-equipped to gather large volumes of data without compromising on quality. To give you an estimated timeframe, we assure you that we can deliver the results well before your given date of 25th September 2026. Choose us for this project and be assured that you'll receive clean datasets ready for comprehensive analysis. With our strong technical background, combined with a commitment to clear communication and on-time delivery

@manishashree12

Hello there, I can assist you with the web scraping and sentiment analysis project by gathering 500 customer reviews from various platforms like Google Reviews, Reddit, TripAdvisor, Instagram, YouTube, TikTok, and relevant forums. I will extract the required fields and provide two Excel files as specified in the project description. My expertise includes data scraping from all the mentioned sources, ensuring accurate and detailed results. I aim to deliver the output within the specified timeframe on 25 September 2026. Let's discuss further details to ensure a successful completion. Thanks,

@emrahg7

Hi, I'd start with the scraping setup for each source first, since that's what determines everything else, if a site blocks bots or changes its layout, I need to know that before I promise you a number. For this job I can pull from Google Reviews, TripAdvisor, Reddit and X reliably. Instagram, YouTube and TikTok are trickier since they limit public data pretty hard, so I'd treat those as a smaller supplemental slice rather than a main source, likely 50-100 combined. Once the raw data's in, I dedupe it, then run the reviews through sentiment and topic classification and split it into your two Excel files exactly as you laid out, review-level and topic-level, with the fields you listed. Reddit and TripAdvisor tend to be the most consistent for volume, Google Reviews depends heavily on the location count you're targeting. I can have the full 500+ reviews scraped, classified and delivered in 2 days. One thing I'd flag: the September 25 2026 extraction date means this needs to run live that day, not before or after, so I'll need the source list locked in ahead of time. What's the target business or brand, so I can confirm review volume is realistic across those platforms? Best, Emrah

@sakline00

Hi, I can collect 500+ unique public reviews across relevant sources, clean duplicates, and deliver both Excel datasets with category, topic, sentiment, intensity, and evidence classification. I’ll provide a source-by-source volume estimate after reviewing the target brand/listings and complete extraction on September 25. Which brand/business and locations should be analyzed? Let's discuss in chat to proceed further.