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title: Mapping Public Sentiment: GIS Hotspot Analysis of Trendy...
description: A Final Year Project that builds an end-to-end Python pipeline to map public sentiment from Google Maps reviews across Kuala Lumpur. The workflow scrapes...
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# Mapping Public Sentiment: GIS Hotspot Analysis of Trendy Spots in Kuala Lumpur

**[Medium](https://daily.dev/sources/medium_js)** · 4 min read · 0 upvotes · 0 comments

## Summary

A Final Year Project that builds an end-to-end Python pipeline to map public sentiment from Google Maps reviews across Kuala Lumpur. The workflow scrapes reviews, preprocesses text with regex and tokenization, scores sentiment using the SentiLexM lexicon (designed for Malay/English code-switching), geocodes establishments, and renders an interactive WebGIS heatmap using Folium deployed on Netlify. Key findings show positive sentiment clusters around transit-connected central city areas, with applications in commercial real estate and urban planning.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@muhammaduzairaqeel/mapping-public-sentiment-gis-hotspot-analysis-of-trendy-spots-in-kuala-lumpur-660bd5281d0b>

## Questions this post answers

### How can I combine sentiment analysis with GIS to visualize sentiment hotspots on a map?

Scrape reviews (e.g., from Google Maps), clean the text with regex to remove URLs, punctuation, and casing noise, then score sentiment using a lexicon like SentiLexM that handles local slang and code-switching. Geocode each review location, then feed latitude, longitude, and sentiment score into Folium's HeatMap plugin to render an interactive Kernel Density Estimation hotspot map, deployable as static HTML on Netlify.

_Developers building similar geospatial sentiment pipelines can find related Python and GIS workflows on daily.dev._

### Why would I use a lexicon like SentiLexM instead of a standard English sentiment dictionary?

Standard English sentiment dictionaries often fail to correctly score reviews containing local slang or code-switching between languages, such as Malay and English mixed in the same sentence. SentiLexM is a lexicon dictionary designed to account for this local context, assigning emotional weights to words while accounting for regional dialect and slang that off-the-shelf analyzers misinterpret.

_Anyone choosing a sentiment lexicon for multilingual text can compare approaches like this on daily.dev._

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- [Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM](https://daily.dev/posts/building-an-end-to-end-sentiment-analysis-pipeline-with-scikit-llm-dh45aqfbd) · Machine Learning Mastery · 2 upvotes · 0 comments
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---

Tags: [#python](https://daily.dev/tags/python), [#nlp](https://daily.dev/tags/nlp), [#sentiment-analysis](https://daily.dev/tags/sentiment-analysis), [#gis](https://daily.dev/tags/gis)

[View this post on daily.dev](https://daily.dev/posts/mapping-public-sentiment-gis-hotspot-analysis-of-trendy-spots-in-kuala-lumpur-ccqvycodr)

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