Customer Reviews Sentiment Analysis

@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 Matlab, Python, and similar tools. 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

@nrajuu07

As a seasoned Machine Learning Engineer, I have successfully worked on various data-driven projects, from medical image analysis to time series forecasting. I understand the nuances and complexities of building reliable models, providing relevant classifications, and maintaining solid understanding at each step. My skills in Python align nicely with your project requirements, and I'm well versed with popular sentiment analysis libraries such as spaCy, NLTK, TextBlob, as well as Hugging Face transformers. I believe that my ability to deliver production-ready AI systems built on real-world data while providing clear documentation every step of the way sets me apart as a freelancer. Having met or exceeded acceptance criteria like you've mentioned in previous projects gives me an edge over others. I can create a -10% holdout sample from your dataset with a macro-F1 score not less than 0.80 for an extra layer of assurance. Don't just take my words on this; I welcome you to explore my portfolio for relevant samples that reflect my competence in transforming complex data into actionable insights for improved customer experience. Can't wait to discuss implementation details to embark on this journey together!

@adriank06

Hi there, What I can do for your project is as follows: 1- First I will read and clean and normalize all text files (like lowercasing, punctuation removal, tokenization). 2. Implement a traditional method for sentiment anaysis (probably TextBlob or VADER) and use them as a fast baseline. 3. Evaluate open‑source transformer models like distilbert-base-uncased-finetuned-sst-2 or roberta-base-sentiment for higher accuracy. The model also will return a confidence score. 4- Compare results using accuracy, precision/recall, and consistency. 5- Select the method that gives the best accuracy while meeting cost and deployment constraints. Note that we can also use openai APIs for this task, so, if you are interested let me know, so, I will explain how I will implement it. What I will deliver are: 1- A script that runs locally with a single command and fully commented 2- A markdown report discussing the comparison results and best model performance as well as main parts of the script Please let me know if you have any question about any part. Bests, Adrian

@pankajavi161

Hi, I’m Pankaj Kumar Maury, a Data Analyst & ML Engineer with 5+ years of experience in NLP and sentiment analysis. I’ve previously built transformer-based pipelines (BERT, Hugging Face) to classify customer reviews with high accuracy, so your project aligns perfectly with my expertise. I will process all your .txt files, clean and normalize the text, and apply a robust sentiment model (preferably fine-tuned transformer for best accuracy). Each review will be labeled as Positive, Neutral, or Negative with a confidence score. You’ll receive a clean CSV including file name, full text, sentiment, and confidence. Additionally, I’ll provide a well-documented Jupyter Notebook/Markdown report explaining preprocessing, model selection, evaluation, and visualizations of sentiment distribution. I will ensure reproducibility with a one-command script and requirements.txt. For validation, I’ll create a 10% hold-out dataset and optimize the model to achieve ≥0.80 macro-F1 score. I’ve delivered similar NLP solutions before and can share examples. Let’s build a reliable sentiment system for your business.

@jerrywise01

Data analysis | fast and reliable | pay less | Client satisfactory guaranteed Replies within hours

@joshika793

Hi, I can build a clean, one-command pipeline to classify your `.txt` reviews with high accuracy. I’ll use a transformer-based sentiment model (Hugging Face) for reliable results, with proper text cleaning and evaluation to meet your ≥0.80 macro-F1 requirement on a 10% hold-out sample. I’ve done similar review-analysis work before and focus on both accuracy and clarity of output. Happy to run a quick sample if you share a few files. Thanks

@pawansaini10101

Yeah it's an easy project i can do this for you in the budget and in a day as per your requirements. Let's connect for more info.

@yousefm51

Hi sir,I will build a Python pipeline that reads all review text files, applies a high-accuracy transformer-based sentiment model, and outputs a clean CSV with sentiment labels and confidence scores. The solution will run in one command with a requirements file, and include a notebook report showing methodology and sentiment distribution visualization. If needed, I can also create a labeled subset to properly evaluate model performance and report reliable metrics.

@aladdin21

I've read your requirements carefully. You need a reliable, reproducible sentiment analysis pipeline that processes plain .txt reviews and outputs a clean CSV with labels + confidence scores. As a Big Data engineer, I've built similar sentiment analysis pipelines before — in fact, one of my portfolio projects involves Arabic dialect sentiment analysis on election data using multiple models (SVM, XGBoost, Random Forest, MLP) with MLflow tracking. I've also worked on enterprise Big Data projects including Cloudera-based banking platforms and Data Lakehouse architectures, which means I can easily scale your solution if you have hundreds or thousands of review files. For your English customer reviews, I'll use proven NLP approaches (Hugging Face transformers or spaCy) to ensure accuracy, deliver a clean CSV with sentiment labels and confidence scores, and provide a Markdown report with visualizations — all meeting your 0.80 macro-F1 requirement.

@Sinergiavitalsas

nice all the sort of sentiments ? prtfolio on syte and youtube evidences by ceo name "john jairo sanabria sarmiento" work on sentiments linked mostly to stock market

@mahakt21

I have strong experience in Python, Natural Language Processing, Machine Learning, and sentiment analysis using libraries such as spaCy, NLTK, TextBlob, and Hugging Face transformer models. I have worked on text classification, document analysis, and research-based NLP projects where accuracy and clean output were critical. I can build a complete sentiment analysis pipeline for your customer review dataset by reading all .txt files, cleaning and preprocessing the content, and applying a reliable sentiment classification model that labels each review as Positive, Neutral, or Negative along with a confidence score. To ensure strong performance and meet the required macro-F1 score target, I can evaluate both traditional NLP approaches and transformer-based models, selecting the best-performing solution for your dataset. The final output will be a clean CSV containing file name, full review text, sentiment label, and confidence score for every review. I value quality work, fast communication, and reliable results. I would be happy to help turn your customer reviews into actionable sentiment insights.

@ayushpro99

i am good product buyer with frequent review relied and i take long time to read and understand reviews