Hinglish Sentiment Analyzer Development Needed

@nrajuu07

As an experienced Machine Learning Engineer with a specific focus on Natural Language Processing (NLP) tasks, I am confident in my ability to complete this Hinglish Sentiment Analysis project to an exceptional standard. For the given task, I would suggest utilizing FastText or IndicBERT as they offer strong multilingual capabilities. In terms of the methodology, I will incorporate text normalization, language-switch detection and sentiment score calculation into the NLP pipeline, giving you a comprehensive solution. This approach ensures that not only will you receive accurate sentiment labels, but also get an insight into when and where the language switches occur - essential to your project's objectives. To sum it up - A single proficient programmer ready to help you succeed on time. Let's create a robust sentiment analysis tool for Hinglish social media posts that not only meets your expectations but exceeds them! I look forward to discussing more details about your project.

@avinashdubeyg209

Project Proposal: Hinglish Sentiment Analysis with Language-Switch Detection Objective: This project aims to develop a compact Natural Language Processing (NLP) system that analyzes the sentiment of social media posts written in Hinglish (a blend of Hindi and English). The system will not only determine the sentiment score but also identify instances and locations of language switches between Hindi and English in the text. Scope and Features: 1. **Sentiment Analysis**: - Develop a model capable of classifying the sentiment of Hinglish text into categories (e.g., positive, negative, neutral). - Emphasize accurate sentiment detection despite the informal, mixed-language nature of the text. 2. **Language-Switch Detection**: - Implement a mechanism to identify and highlight where the text shifts between Hindi and English. - Provide a simple interface that visually differentiates language segments within the text. 3. **Text Normalization (Basic)**: - Apply basic text pre-processing techniques to handle common

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@toqeer74

प्रोजेक्ट में आपके लिए प्राथमिक चुनौती यह है कि आपको जल्द-से-जल्द एक प्रभावी सेंटिमेंट एनालाइजर जरुरत है जो Hinglish में सामान्य सोशल मीडिया पोस्ट्स को समझ सके। मैं FastText या IndicBERT का उपयोग करते हुए एक सम्पूर्ण NLP पाइपलाइन को निर्माण करूंगा, जिसमें टेक्स्ट की सफाई, भाषा स्विच का पता लगाना और सेंटिमेंट स्कोरिंग शामिल है। मैं एक कोड नोटबुक तैयार करूंगा, जो रॉ पोस्ट्स को लेते हुए सेंटिमेंट लेबल और भाषा परिवर्तन के स्थानों को प्रदर्शित करेगा। इस एक-दिन के कार्यात्मक समय में, मैं सटीकता का प्रदर्शन करते हुए सभी आवश्यक डिलीवरी तैयार करूंगा। क्या हम इस सप्ताह एक 10-मिनट की कॉल पर चर्चा कर सकते हैं?

@Asadullah401

Utilizing a perfect cocktail of Python expertise and seasoned automation skills, I am confident that I can not only meet but exceed your expectations for this NLP project. In line with your lightweight approach using off-the-shelf embeddings, I propose combining my vast experience with FastText, IndicBERT, and multilingual transformers to ensure reliable sentiment analysis while highlighting the language-switch positions, pertinent to Hinglish. This approach will effectively enable your compact project to run seamlessly without any cloud dependency. Drawing from my history working on complex, real-world systems like automation-driven applications and computer vision-based solutions, I possess an undeniable ability to engineer long-lasting and stable solutions. Your Python notebook or script with well-commented and maintainable code is my bread and butter. Throughout the project’s execution, I will continuously prioritize end results aligned with your desires- accurate sentiment labels and no cloud dependencies. My ultimate focus is to deliver projects like yours that create OR measure value moving beyond finding quick fix

@robertr375

Hello there , Good afternoon! I am professional mobile programmer with skills including Pandas, Sentiment Analysis, Deep Learning, Python, Natural Language Processing, NLP and NumPy. Please contact me to discuss more regarding this project. Thanks & Regards

@aymenb82

Hi, I can deliver this today! I have built sentiment analysis systems using Python and NLP. My experience includes: - Complete sentiment analysis pipeline with Python - NLP processing with multiple languages - Deep learning text classification - Clean, well-documented code ready to submit I can provide: - Working Python code with NumPy and Pandas - Trained model for Hinglish sentiment detection - Documentation and comments - Ready to submit format Let's start now - I can deliver within hours. Ayman

@habibaa323

Hello, I understand your project and I can build a simple NLP system for Hinglish social media text that performs sentiment analysis and basic language-switch detection. I have previously worked on a similar sentiment analysis project where I classified movie reviews as positive or negative using an LSTM model, so I am familiar with text processing and sentiment tasks. For this project, I will follow a clear and lightweight approach. First, I will preprocess the text by cleaning URLs, emojis, and noisy slang words. Then I will detect language switching between Hindi and English using a simple rule-based or token-level method. After that, I will apply a pretrained multilingual model such as IndicBERT, mBERT, or FastText to classify sentiment into positive, negative, or neutral. The output: will include sentiment label, language switching points, and cleaned text. I will deliver everything within 2–3 days and provide a clean, well-commented Python notebook or script, a short README with setup instructions and explanation, and sample results on social media data. Best regards

@filipig6

Hi, I can deliver a clean and fully working MVP by tomorrow that meets your requirements and runs end-to-end in a single notebook. What I will include: * Sentiment analysis for Hinglish posts (positive / neutral / negative) using a lightweight pre-trained multilingual model * Basic language switch detection (Hindi ↔ English) using rule-based token analysis * Simple preprocessing (cleaning, normalization) * Fully commented Jupyter Notebook * Small README explaining setup and methodology * Sample run with your provided social media examples Approach: I will prioritize speed, clarity, and reproducibility, using lightweight NLP tools (no heavy training or cloud dependencies) so the project runs immediately on any local machine. Delivery: * Ready within 24 hours * Fully executable notebook with clear outputs Given the tight deadline, this will be a focused and efficient implementation, designed to work reliably and be easy to present for academic submission. Let me know if you’d like me to proceed. Best, Filipi

@vineet0903

Hi! I've read your requirements carefully — I can deliver this before your deadline. Approach: I'll use `cardiffnlp/twitter-xlm-roberta-base-sentiment` (multilingual, works great on Hinglish) for sentiment scoring, paired with langdetect + a Hindi word list for token-level language-switch detection. Runs fully offline in a single Jupyter notebook. Steps: 1. Normalize text (remove @mentions, URLs, hashtags) 2. Tag each token as EN/HI for language-switch positions 3. Run sentiment model → positive / neutral / negative + confidence score 4. Sample run on your Twitter, Facebook, Instagram snippets included ✅ Deliverables: * Well-commented .ipynb notebook (end-to-end, no cloud dependencies) * README with pip install steps + methodology explanation * Clean output showing sentiment labels + language-switch positions I'll send the notebook for a test run before you sign off. If output seems off, I'll fix it. Can you confirm the format of your sample data?

@lutherl1

Hello, I’m confident I can deliver exactly what you’re looking for. I understand you need a clean, user-friendly NLP project focused on sentiment analysis of Hinglish social media posts, with added language-switch detection and text normalization to support it. I’ll leverage FastText embeddings for lightweight, offline sentiment scoring combined with a rule-based method for detecting Hindi-English switches using Unicode ranges and simple token matching. Text normalization will include minimal preprocessing like lowercasing and punctuation removal. The process will be: data ingestion → normalization → language switch tagging → embedding vectorization → sentiment classification → output results with switch positions. I’ll evaluate accuracy by comparing labeled test snippets and showing clear outputs in a single, well-commented Python notebook. I’d love to discuss this more and help ensure your project runs smoothly on your local setup. Regards, Luther

@ayushdebnath8

I can complete this Hinglish NLP project within your one-day deadline with a simple and reliable approach. I’ll use a lightweight multilingual model for sentiment analysis (positive/neutral/negative). The solution will include preprocessing, sentiment scoring, and clear output showing language transitions. I’ll test it on your provided social media samples to ensure sensible results. Deliverables will include a well-commented Python notebook, README, and sample outputs—all running locally without dependencies. I can start immediately and deliver within a few hours.

@sarishtshreshth

Hi! I work in NLP with a focus on text analysis and code-mixed Indian languages. A bit about my background: I've previously built a plagiarism detection system — which gave me hands-on experience with text similarity, embedding spaces, and document-level language modelling. I've also co-authored / written an NLP research paper, so I understand not just the implementation side but also how to evaluate, benchmark, and report results rigorously. For this project specifically, I'd use XLM-RoBERTa (Cardiff NLP's Twitter-fine-tuned variant) — pre-trained on 198M multilingual tweets, so it handles romanised Hindi slang like "bakwaas", "mast", "theek hai" natively without any extra labelling effort from your side. My pipeline covers: Text normalization (URL/mention stripping, character-stretch collapse) Per-token language detection using Unicode block analysis → compact span-level switch map 3-class sentiment scoring (Positive / Neutral / Negative) with per-class confidence scores Clean output as a Pandas DataFrame / CSV — no cloud dependencies (pip install transformers torch pandas) I've already built a working version of this exact pipeline. Happy to run it on your sample posts and share the output before you award — so you can verify quality first.

@jhashouvik

Hi, For Hinglish sentiment analysis on a tight deadline, I'd use roberta-base-sentiment, pre-trained on multilingual social media text, handles Hindi-English code-switching without fine-tuning. What I'll deliver: Single Jupyter notebook: normalization → language-switch detection (langdetect, token-level) → sentiment scoring (Positive/Neutral/Negative + confidence) README with setup instructions and methodology explanation Sample run on your Twitter, Facebook, and Instagram snippets with a clean output table Everything runs fully local. With 10+ years in AI/ML and hands-on experience building NLP and GenAI pipelines, I can turn this around in 4–6 hours, giving you time to review before submission.

@Sanjaykumar19046

I will build a compact Hinglish Sentiment Analyzer using Python with HuggingFace Transformers, NumPy, and Pandas. For sentiment analysis, I will use a pretrained multilingual model like XLM-RoBERTa (cardiffnlp/twitter-xlm-roberta-base-sentiment), ensuring accurate predictions on mixed Hindi-English text without training. The pipeline includes: (1) basic text cleaning (lowercasing, removing noise), (2) lightweight Hinglish handling, and (3) rule-based language detection using a small Hindi word dictionary to identify language switches at the token level. Sentiment is classified into positive, neutral, or negative with confidence scores. The final deliverable will be a well-structured Python notebook/script that takes raw social media text and outputs sentiment along with language sequence and switch points. A simple README with setup steps and methodology will be included. Accuracy will be demonstrated using curated sample sentences reflecting real Hinglish usage from Twitter, Facebook, and Instagram, ensuring reliable end-to-end execution within a one-day timeline.

@mariumbawany1234

Hi, I can help you complete this NLP project quickly and clearly. I’ll build a simple Python notebook that takes Hinglish text, cleans it, detects Hindi–English switches, and gives sentiment (positive/neutral/negative). I’ll use lightweight models like FastText or a multilingual transformer so everything runs smoothly on your local system without any cloud setup. You’ll get clean, well-commented code, a small README with easy setup steps, and a sample run on your social media data. I’ll keep the approach simple but effective so it’s easy for you to explain in class. Since the deadline is tight, I can deliver within 1 day. Ready to start immediately.

@mrbhautik

This project aims to develop a compact Natural Language Processing (NLP) system that analyzes the sentiment of social media posts written in Hinglish (a blend of Hindi and English). The system will not only determine the sentiment score but also identify instances and locations of language switches between Hindi and English in the text. Scope and Features: 1. Sentiment Analysis: - Develop a model capable of classifying the sentiment of Hinglish text into categories (e.g., positive, negative, neutral). - Emphasize accurate sentiment detection despite the informal, mixed-language nature of the text. 2. Language-Switch Detection: - Implement a mechanism to identify and highlight where the text shifts between Hindi and English. - Provide a simple interface that visually differentiates language segments within the text. 3. Text Normalization (Basic): - Apply basic text pre-processing techniques to handle common

@shokoufeh1994

Sentiment analysis for code-mixed languages like Hinglish requires a different approach than standard English NLP. Pre-trained multilingual models like XLM-RoBERTa or MuRIL handle the Hindi-English mixing well as a base, and fine-tuning on labeled Hinglish sentiment data significantly improves accuracy. I'd build this in Python using HuggingFace Transformers — load a multilingual base model, prepare a labeled dataset, fine-tune, and wrap in a clean inference pipeline. I'd also handle preprocessing quirks specific to Hinglish like Roman Hindi transliteration. Do you have an existing labeled dataset, or do you need help with data collection and labeling too? Also, what deployment format are you targeting — API, notebook, or standalone script?