I will do nlp text classification and sentiment analysis using python
Machine learning engineer
Level 2
Has met high performance criteria and has a proven track record for meeting client expectations.
About this Gig
I will do NLP text classification and sentiment analysis using Python
Struggling to make sense of large volumes of text data? I help businesses turn raw text into clear, actionable insights using Natural Language Processing (NLP) and Python.
What I offer:
Text classification (spam detection, topic labelling, categorisation)
Sentiment analysis (positive, negative, neutral detection)
Data cleaning & preprocessing
Model building using Python (Scikit-learn, NLTK, spaCy, or BERT)
Clear reports with accuracy scores and visualisations
Why choose me?
️ Clean, well-documented Python code
️ Accurate and reliable results
️ Fast turnaround
️ Clear communication throughout
Whether you're analysing customer reviews, social media posts, or survey responses, I'll deliver a solution tailored to your data.
NLP,Natural Language Processing,Text classification,Sentiment analysis,Python,Machine learning,Text mining,Data preprocessing,Text data analysis,Data cleaning,Model accuracy,Text analytics,Customer review analysis,Social media sentiment,Survey data analysis,Spam detection,Topic classification
Message me before ordering if you have a custom requirement!
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FAQ
What file formats can I send you for text classification or sentiment analysis?
I accept text data in CSV, Excel, JSON, or plain text (.txt) formats. If your data is in a different format, feel free to message me before ordering, and I'll confirm whether I can work with it.
Do I need to have labelled data, or can you help me label it too?
Labelled data isn't always required. If your dataset is unlabelled, I can assist with labelling as part of the project, depending on the size and complexity — just let me know in advance so I can recommend the right package.
Will I receive the source code and a report explaining the results?
Yes, every order includes the complete Python source code along with a clear report showing model accuracy and key insights, so you can understand exactly how the results were generated and reuse the code if needed.

