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- Tasks: Dive into sentiment analysis of COP26 using Python and Twitter data.
- Company: Join a forward-thinking team focused on climate change solutions.
- Benefits: Flexible work options, hands-on experience, and potential for impactful contributions.
- Why this job: Be part of a meaningful project that influences climate discussions globally.
- Qualifications: Basic knowledge of Python and interest in machine learning required.
- Other info: Opportunity to develop skills in data analysis and ML with real-world applications.
The predicted salary is between 30000 - 42000 ÂŁ per year.
Problem statement: 2021 UN Climate Change Conference event COP26 has been held in Glasgow, Scotland, UK. Analyze sentiments of people on this topic relative to the effectiveness of the summit.
Resources used: Jupyter Notebook as an IDE, Twitter API v1.1, NLTK, Tweepy and TextBlob library and Python 3.8.
Approach: My objective here is to create a PoC for “Sentiment Analysis about Climate Summit effectiveness – COP26 Summit Model” while adopting a “minimal coding with maximum efficiency” approach. We take tweets from Twitter by querying text like “COP26”, storing in a file and data frame, calling NLTK/TextBlob package/method to score sentiments against each text, and finally display relevant output. The output should provide a reasonable indication (at minimum qualitatively) to pull off a full-blown ML model investment. Let’s dive into making stage 1 analytics.
Step 1: Create App in Twitter and save API key and secret code for later use following steps below: Create an App in the same account (You give a name to APP under project name). Store all credentials such as API Key, Secret code, Bearer Token etc. (Tokens & Keys are generated from the previous step). Test some of Twitter API endpoints (from its LAB with V2 versions which is still evolving) such as “Recent Search”.
Step 2: Coding in Jupyter Notebook or any IDE that supports Python 3.6 and above. Check tweets with extreme sentiment values. TextBlob reads each sentence and outputs sentiment score between -1 to +1 as negative to positive sentiments.
Summary: Sentiment analysis about COP26 summit doesn’t end here; we have just scratched the surface from an analysis standpoint. There are various other dimensions that I didn’t capture here such as views about COP26 summit across countries, demography, occupations, communities, etc. Twitter captures tons of metadata (e.g., location, time, followers, likes, retweets, etc.) while we tweet. We can further enhance the level of analysis by adding business, political, time-series context to name a few. The main purpose is to highlight ease and speed in completing ML PoC to take further business decisions without putting much effort into coding and building expensive data pipelines. Hope you find this approach useful in applying in the journey of digital transformation by creating small use cases or PoC solutions before going full-blown in product development.
Contact Detail:
Techmanthan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Sentiment Analysis (Climate Summit-COP26): Jumpstart with ML coding
✨Tip Number 1
Familiarise yourself with the Twitter API and its endpoints. Understanding how to effectively query tweets will give you a significant advantage in your analysis, especially when it comes to gathering relevant data for sentiment analysis.
✨Tip Number 2
Brush up on your Python skills, particularly with libraries like NLTK and TextBlob. Being comfortable with these tools will help you implement sentiment scoring efficiently and effectively during your project.
✨Tip Number 3
Engage with online communities or forums focused on machine learning and sentiment analysis. Networking with others in the field can provide insights, tips, and even potential collaboration opportunities that could enhance your project.
✨Tip Number 4
Consider creating a small demo or proof of concept (PoC) before applying. This hands-on experience not only solidifies your understanding but also showcases your initiative and practical skills to us when you apply.
We think you need these skills to ace Sentiment Analysis (Climate Summit-COP26): Jumpstart with ML coding
Some tips for your application 🫡
Understand the Job Requirements: Read the job description carefully to understand the specific skills and tools required for the role. Make sure to highlight your experience with Jupyter Notebook, Python, and sentiment analysis in your application.
Showcase Relevant Experience: In your CV and cover letter, emphasise any previous projects or experiences related to sentiment analysis, machine learning, or using Twitter API. Provide examples of how you have successfully implemented similar projects.
Tailor Your Application: Customise your CV and cover letter to reflect the language and keywords used in the job description. This will help demonstrate that you are a good fit for the position and understand the company's needs.
Proofread Your Application: Before submitting, make sure to proofread your application for any spelling or grammatical errors. A well-written application reflects your attention to detail and professionalism.
How to prepare for a job interview at Techmanthan
✨Know Your Tools
Familiarise yourself with the tools mentioned in the job description, such as Jupyter Notebook, Twitter API, NLTK, and TextBlob. Be prepared to discuss how you've used these tools in past projects or how you would approach using them for sentiment analysis.
✨Understand the Problem Statement
Make sure you fully grasp the problem statement regarding COP26 and sentiment analysis. Think about potential challenges and solutions related to analysing sentiments on social media, and be ready to share your insights during the interview.
✨Showcase Your Coding Skills
Since the role involves minimal coding for maximum efficiency, be prepared to demonstrate your coding skills in Python. You might be asked to solve a coding challenge or explain your thought process while coding, so practice coding problems related to data analysis and sentiment scoring.
✨Discuss Broader Implications
Be ready to talk about the broader implications of your analysis. Consider how sentiment analysis can influence business decisions and public perception, especially in the context of climate change. This shows that you understand the significance of your work beyond just the technical aspects.
