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I will perform nlp and sentiment analysis on your data
Data Scientist
I am a Full Stack Developer and Data Scientist with a background in research and statistical analysis. I help students, researchers, and businesses make sense of their data — from hypothesis testing a...
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Your customers are already telling you what they think in product reviews, support tickets, survey responses, and social media. I'll turn that unstructured text into structured, actionable insight using natural language processing.
What I can do:
- Sentiment analysis: positive, negative, and neutral classification
- Topic modeling: LDA and BERTopic to discover recurring themes
- Text classification and custom categorization
- Keyword and key phrase extraction
- Named entity recognition (NER): people, locations, organizations
- Word frequency and co-occurrence analysis
What you'll receive:
- Python notebook with full NLP pipeline
- Labeled output dataset with sentiment scores (CSV)
- Visualizations: word clouds, sentiment distribution charts, topic clusters
- Summary report with key findings and business interpretation
To get started, I'll need:
- Your text dataset in CSV or Excel format (reviews, comments, survey responses, etc.)
- The language of the text (English preferred; Indonesian available)
- Your specific analysis goal or business question
Not included: data scraping or collection, real-time API or monitoring tools, social media account access.
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FAQ
What format does my text data need to be in?
CSV or Excel with your text in one column. It can be product reviews, customer feedback, survey open-ends, social media comments, or any other text — as long as it's in a file format I can read.
How many rows of text can you handle?
Up to 50,000 rows is comfortable within the standard package. For larger datasets, message me first so we can agree on scope and timeline.
Can you analyze text in Indonesian?
Yes. Indonesian is available alongside English. If your dataset is in Indonesian, mention it when you order so I can apply the right preprocessing and lexicon tools.
What's the difference between sentiment analysis and topic modeling?
Sentiment analysis tells you whether each piece of text is positive, negative, or neutral. Topic modeling discovers recurring themes across your entire dataset — for example, finding that 40% of negative reviews mention delivery time. Both are available depending on your package.
Can you scrape reviews or social media data for me?
No, data collection is not included. You'll need to provide the dataset. If you need help finding a scraping service, I can point you in the right direction.
Will the output include individual sentiment scores per row?
Yes. The labeled output CSV will include the original text plus a sentiment label and score for each row, so you can filter and analyze further on your own.
