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The goal of this assignment is for you to use the concepts of Natural Language Processing (NLP) and present its results in a clear and engaging manner.
Description:
Introduction to NLP: Visit and familiarize your self with the following site: Huggingface.co
NLP Techniques: Complete the posted assignment below and submit two items.
Applications of NLP: use various AI applications, posted in huggingface.co that require Provide examples for each.
Evaluating NLP Models: measure the effectiveness of NLP models, and determine which tool gave you the best results and were easier than others.
Deliverables:
A PowerPoint presentation covering all the above points.
A presentation session where students present their findings to the class. in a Discussion posted.
Evaluation Criteria:
Understanding of NLP concepts, and use in real-world applications
Clarity and organization of the presentation.
ASSIGNMENT
Assignment: Customer Sentiment Analysis
Objective: Analyze customer reviews and social media posts using natural language processing (NLP) to gauge sentiment towards a particular brand or product. The final deliverable will be a presentation or report showcasing sentiment trends and actionable insights for the business.
Tools and Resources: Use Hugging Face to find these tools and input your information.
http://huggingface.co Search for the models below using sentiment analysis. Watch the video tutorial for using huggingface.
NLTK: NLTK
SpaCy: SpaCy
TextBlob: TextBlob
Vader: Vader
Tutorials:
NLTK Sentiment Analysis Tutorial
SpaCy Quickstart
TextBlob Quickstart
Vader Sentiment Analysis
Assignment Details:
Brand/Product Selection:
Choose a brand or product you are interested in analyzing. This could be a well-known brand or a specific product.
Data Collection:
Collect customer reviews and social media posts related to your chosen brand or product. You can use web scraping tools or APIs to gather this data.
Data Preprocessing:
Clean and preprocess the text data. This may include removing stop words, punctuation, and performing tokenization.
Sentiment Analysis:
Do a search for NLP sentiment analysis in Hugging face, one example is: https://huggingface.co/spaces/pragnakalp/Huggingface_Sentiment_Analysis
Apply sentiment analysis APIs like TextBlob or Vader to gauge the sentiment of the collected data.
Classify the sentiments into categories such as positive, negative, and neutral.
Visualization and Analysis:
Search Huggingface.co for Visualization tools to upload your findings and plot your data:
One example is https://huggingface.co/spaces/biodivx/matplotlib-demo
Create visualizations to represent the sentiment trends using libraries like Matplotlib or Seaborn.
Analyze the sentiment trends to derive actionable insights for the business.
Report Writing:
Compile your findings into a presentation or report. The report should include:
An introduction to the brand/product and the objective of the analysis.
A brief explanation of the data collected and preprocessing steps.
Detailed analysis of the sentiment trends.
Visualizations to support your findings.
Actionable insights and recommendations for the business based on the sentiment analysis.
Deliverables:
Provide a link to the application used to process your findings.
A presentation (in PPT format) or report (in PDF format) detailing your sentiment analysis, insights, and recommendations.
Submission Deadline:
8.12.2024
Rubric (24 Points Total):
CategoryCriteriaPoints1. Data Collection and PreparationRelevance and Quality of Data2 Data Cleaning and Preprocessing22. Sentiment AnalysisUse of NLP Libraries2 Application of Sentiment Analysis APIs33. VisualizationQuality and Clarity of Visualizations2 Integration of Visualizations in the Report/Presentation24. Analysis and InsightsDepth of Analysis3 Actionable Insights and Recommendations35. Report WritingStructure and Organization2 Clarity and Presentation of Findings26. Presentation and SubmissionCompleteness and Professionalism of Submission2
Total Points: 24
This assignment will provide students with practical experience in NLP, sentiment analysis, and deriving business insights from textual data.
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