Multi-Platform Social Sentiment Data Extraction

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@raminde

Have over 18 years of experience in data mining/ Web scrapping/ Scraping Bots/ Chrome/Opera Extensions I have done it all. Tell us your source and we will put it in excel for you, Or we can even give you filtered results as per your requirement, In the format you want. You can also ask for data into a particular format - Excel, Json, Mysql, Databases, XMLs, you name them. Further Can help you with integrating it with ur databases, Can create json outputs. We are not only good with scraping but also with the tools that u may need after that. We can help you build you softwares round the data we have 99% Data Accuracy. We have Duplicate finder. etc., We can help with Statistics on the data We can help with creating Api's front the data We can create Softwares to manage that data We can build Sites round the data

@Prutheus

Let me be direct about the biggest constraint, because it determines what this project can actually deliver: Facebook, Instagram and Snapchat do not permit public comment collection at any meaningful scale, and their APIs only return data for accounts you own or have been granted access to. Twitter/X has a paid API with real limits. YouTube's API is genuinely open and generous. So a solution that "continuously gathers user comments and posts" across all five is not achievable through legitimate means, and anyone promising it is planning to scrape — which gets IPs blocked, breaches platform terms, and stops working without warning. What I can build honestly: - YouTube: full comment and metadata collection via the official API. Reliable and generous quota. - Twitter/X: via the official API within your chosen tier, with the volume limits stated up front. - Facebook and Instagram: your own pages and accounts via the Graph API, plus mentions and hashtag data within Meta's permitted scope. - Everything normalised into one schema with clean text ready for sentiment analysis, deduplicated, with historical backfill where each API allows it. - Scheduled collection with quota-aware backoff, so it keeps running rather than burning through limits. Background: API integrations and 24/7 data pipelines in production for industrial clients. Question: is the sentiment analysis about your own brand and accounts, or broad public listening? For your own accounts the picture is much better than I described. Martin

@TeraByte26

Hi Ayush, I will deliver a Python pipeline for Facebook, Twitter, Instagram, YouTube, and Snapchat, extracting user comments, posts, and hashtags with deduplicated output in CSV or JSON. I commit to a 3-day completion within your budget. Start now, or would you like a free sample? Waiting for your response in chat! Best Regards.

@ashinnd84

Hi, I read your post for "Multi-Platform Social Sentiment Data Extraction" and it lines up closely with the AI / ML work I do day to day. How I would approach it: 1. Agree the success metric before any modelling starts -- accuracy, latency, or cost per call -- so "done" means the same thing to both of us. 2. Stand up a small end-to-end baseline first. You see real output on your data early rather than at the end. 3. Iterate on the baseline, and hand over evaluation scripts plus notes so the numbers are reproducible on your side, not just mine. Directly relevant to your listed skills: API Development, Data Extraction, Java, Machine Learning (ML), Natural Language Processing, Python, Sentiment Analysis, Software Architecture. My bid is ₹31875 against your ₹12500-37500 range, and I can start straight away. Let's connect to discuss this further -- happy to walk you through the approach and cover anything you want nailed down before you decide. Thanks for reading. Best regards, Ashish & Team

@vishal1145

Your pipeline will fail compliance audits if you scrape Facebook and Instagram directly—Meta's Terms of Service explicitly prohibit automated data collection outside their official Graph API, and violating that exposes you to legal risk and permanent IP bans. The second issue is rate-limit orchestration across five platforms with different quota structures; without a centralized queue and exponential backoff, you'll hit 429 errors within hours and lose data continuity. Quick questions - are you planning to apply for official API access from Meta and Twitter, or do you need a workaround architecture that stays compliant? And what's your expected daily volume so I can size the queue and storage layer correctly? Here is the architectural approach: - PYTHON + API DEVELOPMENT: Build a unified ingestion layer using official APIs (Twitter v2, YouTube Data API v3, Meta Graph) with OAuth2 flows, fallback retry logic, and per-platform rate-limit tracking via Redis TTL keys. - DATA EXTRACTION + NLP: Structure the ETL pipeline to normalize raw JSON into your CSV schema, deduplicate by platform-specific post IDs, then run VADER or transformer-based sentiment scoring with confidence thresholds before storage. - SOFTWARE ARCHITECTURE: Deploy as containerized microservices (one per platform) orchestrated by Celery task queues, with PostgreSQL for structured output and S3-compatible object storage for raw payloads, keeping the entire stack portable via Docker Compose. I've built similar compliance-first data pipelines for two fintech clients who needed real-time social signals without violating platform ToS. Let's schedule a 20-minute technical call to map your API access status and finalize the storage schema.

@mahads31

As an experienced full-stack developer with a focus on API development and proficiency in Python, I am confident in my ability to deliver a solution that aligns perfectly with your needs for multi-platform social sentiment data extraction. I understand the importance of respecting each platform's terms of service and rate limits, and can build a robust yet lightweight scraper or API pipeline that does just that. Additionally, my experience with platforms like Node.js, R, and the cloud give me the flexibility needed to make your workflow as portable as possible. For example, I'm comfortable using Redis/SQL storage which lends itself well to handling large amounts of data like what will be generated in this project. This combined with my skills in natural language processing is essential in carrying out accurate sentiment analysis. To further assure you of the quality of my work, I'm happy to adapt your project to use cloud deployment infrastructures such as AWS. I value the transparency and collaborative nature behind lasting client relationships, which is reflected by my plan to give you access to clear documentation and even provide a demo notebook or script so you can visualize how to load the data and check sentiment.

@NEHABHAT92

With over 9 years of expertise in web development and mobile application deployment, I'm confident in my ability to execute your project efficiently, proficiently, and exactly to specifications. My proficiency in Python (your preferred choice but open to Node or R) for scraping and API access means your sentiment-oriented multi-platform social data extraction project is in capable hands. My dedication to creating clean, deduplicated outputs exactly how you want them - CSV or JSON with all the necessary detailed fields - will go a long way in attaining the reliable sentiment analysis you seek. Moreover, I understand the crucial need for adaptability and long-term compatibility of such projects. Your request for a simple configuration file that allows modification of keywords, hashtags or date ranges resonates with my own commitment to user-friendliness and accessibility. Additionally, if it helps streamline the workflow or ensures smooth maneuverability, I can deploy my skills in natural language processing, Redis/SQL storage or cloud deployment. What sets me apart is not just my technical capabilities but also a genuine commitment to provide quality work that elevates the performance of my clients' businesses. I hope you consider me for this exciting project with its potential for wide-ranging benefits!

@Abror86

Hi-Abror Here From Uzbekistan. "BUILD A COMPLIANT SOCIAL SENTIMENT DATA PIPELINE" - "You want compliant multi-platform data extraction with deduplication, configurable sources, anonymized identifiers, and sentiment-ready outputs." I can build a Python pipeline using official APIs where available, rate-limit handling, configurable keywords, deduplication, and structured CSV/JSON output. I will document setup, demonstrate sentiment analysis, anonymize identifiers, respect platform requirements, and make the workflow portable for local execution and future expansion. Could you confirm which platform APIs and historical date ranges you already have access to for this pipeline? Looking forward to working with you.

@alluankit01

Hi, I can build a lightweight Python-based pipeline to collect social posts/comments, hashtags, topic trends, and engagement data for sentiment analysis using official APIs or permitted public data access. The best solution is to first review your target platforms, keywords, hashtags, date ranges, required history depth, and API access availability. Then I’ll build a configurable pipeline that respects platform rate limits, avoids duplicates, anonymizes user IDs, and exports clean CSV/JSON data ready for NLP analysis. I’m comfortable with Python, API integration, data extraction, NLP preprocessing, sentiment analysis, CSV/JSON pipelines, Redis/SQL storage, deduplication, config-driven workflows, notebooks, and documentation. Deliverables will include: * API-based extraction pipeline * Config file for keywords/date ranges * Posts/comments collection * Hashtag/topic extraction * Engagement metric capture * Anonymized user IDs * Deduplicated CSV/JSON output * Sample sentiment notebook/script * Error handling and logging * Clear setup README I’ll focus on a portable, maintainable, and compliant workflow that gathers reliable data without unsafe scraping or rate-limit bypassing, so you can run accurate sentiment analysis after collection. Best regards Ankit

@CauaAlves

The part that breaks first on multi-platform scraping is rate limiting and API policy drift, especially on Instagram and X where scraping terms are stricter than the others. I'd build a Python pipeline with per-platform collectors (official APIs where available, careful scraping where not), a shared schema for posts/comments/hashtags so downstream sentiment analysis doesn't care which platform the row came from, and a scheduler with backoff so you don't get IP-banned mid-collection. You'd get the pipeline, a sample dataset and docs on how to extend it. Which platforms are the top priority to get running first, and do you have API access already for any of them (Meta, X, YouTube)?

@patodeh

Extracting social sentiment data from multiple platforms requires a robust pipeline that gathers user comments, posts, and topic trends while respecting rate limits and terms of service. I will build a Python-based scraper or API pipeline that collects data from Facebook, Twitter, Instagram, YouTube, and Snapchat, providing clean output in CSV or JSON format with necessary fields. I will deliver a simple configuration file for easy modification of keywords, hashtags, and date ranges. I will provide clear documentation and a demo notebook for loading data and running sample sentiment checks. What is the preferred method for handling historical data that may be inaccessible due to platform limitations? I will set up a solution that meets these requirements and propose we discuss the details to lock in the scope and get started.

@dushyant317

I can build a Python-based social-media data pipeline using official APIs where available, with rate-limit compliance, deduplication, anonymized IDs, configurable keywords/date ranges, CSV/JSON export, and a sentiment-analysis demo with clear documentation.

@shayankhan333

Hi, I can build a Python pipeline that gathers comments, posts, and hashtag trends from Facebook, Twitter (X), Instagram, YouTube, and Snapchat, respecting each platform's rate limits, and output deduplicated CSV or JSON with a config file for keywords and date ranges. I can start today. For the historical slice, I will use official APIs where available and fall back to careful scraping where not. Questions: 1) Which platforms are highest priority? 2) How far back should the historical slice go? Looking forward to discussing further. Regards, Shayan.

@Cruz1217

Coordinating rate limits for Facebook, X, Instagram, YouTube and Snapchat is where most pipelines break. I'll set up a Python-based extractor that toggles between APIs and headless scraping when needed, keeping the flow lightweight and ready to start immediately. A tiny YAML config will let you add keywords or date windows without touching the core code. A common mistake is to store raw posts in a single flat file, which makes deduplication and later analysis painful. I’ll output JSON lines containing platform, timestamp, text, hashtags, engagement counts and a hashed user ID. A short notebook will load the file, run a pandas deduplication step and show a sample sentiment check using TextBlob.

@thiagofoz87

I can build a lightweight multi-platform collection pipeline focused on reliable sentiment-analysis-ready datasets, with emphasis on portability, clean structure, and API/rate-limit compliance. My approach would be to separate ingestion, normalization, and export into independent modules so new platforms, keywords, or filters can be added without changing the core workflow. For sources with stable APIs, I would prioritize official integrations and controlled retry/rate-limit handling. For platforms with restricted access, I would implement compliant scraping or hybrid ingestion strategies where technically viable. The output layer would generate deduplicated CSV/JSON datasets containing platform, timestamp, normalized text, hashtags/topics, engagement metrics, and anonymized user identifiers. I would also include configurable keyword/date filters through a simple YAML or JSON config file. The delivery can include: - Python-based pipeline with modular collectors - Optional Redis/PostgreSQL persistence layer for historical indexing - Logging and retry controls for long-running collection jobs - Example notebook/script demonstrating sentiment analysis flow - Documentation for local execution and future extensions Before starting, I would define platform-by-platform access constraints to ensure the implementation remains stable and maintainable. Final delivery would be tested locally on your environment as requested.

@iterativeTech

As an AI & cloud data engineering specialist, my goal is to drive ROI for my clients through intelligent systems and real-time insights. With extensive experience building scalable, production-ready systems aligned with business objectives, I confidently offer my services for your Multi-Platform Social Sentiment Data Extraction needs. My technical skillset, which includes Python (preferred for this project), Java, and ML, conforms perfectly with the requirements you've outlined. I take great pride in designing and deploying advanced AI/ML models that deliver accurate results amidst high volumes of data—making sentiment analysis not just a possibility but a reality. What truly sets me apart is my proficiency in multiple cloud technologies (AWS, Azure), SQL and NoSQL databases such as Snowflake and Databricks - aspects that augment data extraction capabilities tremendously. Moreover, guaranteeing clean output in CSV or JSON format with deduplicated data will simplify your subsequent sentiment analysis processes. I understand the necessity of maintaining updated configurations across platforms. Hence, I'll ensure that my solution embraces your requirement of an easily adjustable configuration file while never compromising on the robustness of the system.

@petropankovdev

Your five platforms do not sit at the same access level, and that is worth settling first. YouTube gives comments, hashtags and engagement through the official Data API, historical included. X allows recent search on the paid API tier, with how far back you reach depending on the tier you hold. Facebook and Instagram return data through the Graph API only for pages and business accounts you own or hold a token for; open public comment and hashtag search there closed when CrowdTangle and the hashtag endpoints were retired. Snapchat has no public content surface, so it cannot be collected within its terms at all. So I would build the pipeline as you described it, one collector per platform behind a shared interface, and ship YouTube and X live plus Facebook and Instagram for the accounts you can authorise. Snapchat stays a documented gap rather than a stub that quietly returns empty. If you hold access I have not assumed, tell me and I will wire it in. The rest is straightforward. One config file for keywords, hashtags, accounts and date ranges, so adding a term never touches collector code. Output normalised across platforms into the fields you listed, with the user id salted so it stays consistent across runs without being reversible. Deduplication on content hash plus platform id, because the same post returns on re-runs and across overlapping keywords. Per-platform rate limiting with backoff and resume, so a long historical pull survives an interruption instead of restarting. Plus the demo notebook you asked for and setup documentation. Before you commit to anything, I can run one keyword against YouTube and send you the real CSV, so you can check the field shape and the deduplication on actual data rather than on my description of it. I build data collection pipelines regularly. The closest was a delivered harvesting system running across a large number of sources, handling pagination, retries and deduplication at scale into one clean dataset, accepted and paid by the client. On this account I have one completed project rated 5 out of 5, delivered on time and on budget. 15000 INR, 12 days. One question: which keywords or hashtags are you starting with, and how far back does the historical slice need to go? That drives the X API tier more than any other decision here. Petro Pankov, BotCraft Group

@aakaashberi

You wrote "respects each platform's terms of service". Taken seriously, that rules out most of your list, and I would rather tell you now than hand you empty CSVs later. Snapchat has no public content API. There is nothing to collect. Any bid that includes it either has not checked or is planning to scrape something it should not. Facebook and Instagram only return data for pages and accounts you own or have been granted access to. There is no public keyword search. If you own the pages, this works properly. If you want to hear what strangers are saying, it does not. X does have full-archive search, but historical access sits behind paid API tiers. That is a monthly subscription in your name, not part of a build fee. Tell me the budget and I will size the historical slice to fit it. YouTube is the one that works cleanly: search, comments and engagement through the Data API, with a daily quota I design around rather than hit. So I build exactly the pipeline you specified - config file, dedupe, CSV and JSON output, storage, documentation, demo notebook - running properly on YouTube and X, with Facebook and Instagram written as adapters that switch on the day you connect owned pages. Same architecture, honest coverage. User IDs hashed at ingest, as you asked. Do you own the Facebook and Instagram accounts you want covered? That single answer decides half the scope.