代写DTS406TC Natur al Language Processing Coursework 1帮做Python编程

Module code and Title	DTS406TC Natural Language Processing
School Title	School of Artificial Intelligence and Advanced Computing
Assignment Title	Coursework 1 (CW 1)
Submission Deadline	5 pm China time (UTC+8 Beijing) on March23,2025
Final Word Count	3000
If you  agree to  let the university use your work  anonymously  for teaching  and learning purposes, please type “yes” here.

Policy

The report should be submitted in PDF format. The word count limit of the report is 3,000. All code must be written  in  Python.  It  should  be well-structured,  easy  to  read,  and  thoroughly documented. Each function should include comments explaining its purpose and functionality. Ensure that the code runs without errors. All required files should be included in a single zip file. A README  file is needed to explain how to run the  code and list any dependencies. The document must be submitted through Learning Mall Online to the appropriate drop box.

DTS406TC Natural Language Processing

Coursework 1 (Group Assessment)

Due: 5:00 pm China time (UTC+8 Beijing) on March23,2025

Weight: 40%

Maximum score: 100 marks (80 %  group marks + 20 %  individual marks)

Groupings: Each  group  consists  of 2-3  students.  You  are  free  to  select  your  own team members. Students who do not make a selection will be randomly assigned to a team . Once the teams are confirmed , no changes will be permitted.

Assessed learning outcomes:

A Systematically comprehend the theoretical foundations ofNatural Language Processing B Apply statistical  and   machine  learning techniques to process   and   analyze  natural language data

Overview

Sentiment Analysis is the process of determining whether textual content expresses a positive, neutral, or negative sentiment. With the vast amount of textual data (e.g., tweets, Reddit posts, reviews) generated daily, Sentiment Analysis can automatically identify users' attitudes from User-Generated  Content  (UGC),  assisting  companies  or  organizations  in  making  informed decisions.

1. Literature Review on Sentiment Analysis (20 Marks,  Individual Work)

a)    Overview of the sentiment analysis and its applications. Please provide three examples of real-life applications of sentiment analysis. (6 Marks)

b)   Please list three key challenges in sentiment analysis. (6 Marks)

c)    Please elaborate on two traditional methods (e.g., Naive Bayes, SVM) and two deep learning approaches (e.g., BERT, GPT) on the sentiment analysis. Meanwhile, discuss the advantages and disadvantages of each approach.  (8 Marks)

d) Each   team   member    should   individually   complete the   literature   review   on sentiment analysis. This section will be scored individually.

2. Data Collection (12 Marks, Group Work )

Collect two datasets of User-Generated Content (UGC) from platforms like Twitter, Reddit, or Weibo,  focusing on  sentiment  analysis  in  different  scenarios.  Each  dataset  should  contain  a minimum of 3,000 instances. Preprocess the datasets by performing tasks like stopword removal and tokenization. Finally, a statistical analysis of the two datasets should be conducted (e.g., the word distribution of the corpus). Notice that some UGC data may be downloaded from Kaggle if there are API restrictions preventing direct downloads from social platforms. (6 Marks/dataset x 2=12 Marks)

3. Algorithm Description & Implementation  (48 Marks, Group Work)

a)    Choose four approaches for the sentiment analysis task on the collected UGC datasets: two using traditional methods and two employing deep learning methods.   All  four approaches should be applied to each of the UGC datasets. Please provide the pseudo- code and briefly provide  the  comments for each function of the pseudo-code.  (5 Marks/algorithm x 4= 20 Marks)

b)   Develop a sentiment analysis system for each approach using Python. The implementation pipeline should include the following components: feature engineering (3  Marks,  e.g.,  converting  textual  data  to embedding space regarding to different approaches), algorithm implementation (3 Marks, with fine-tuning required for the deep learning approach), and metrics computation (1 mark). 7 Marks/algorithm x 4 = 28 Marks)

4. Results Analysis (13 Marks, Group Work)

a)    Provide the sentiment analysis results for each approach applied to the two UGC datasets. Select and apply three relevant metrics (e.g., precision, recall, and F 1 score) to assess the performance of the implemented models, with each metric worth 3 Marks. (9 Marks)

b)   Explain the reasons behind the model performance for each approach.    (1 Mark/algorithm x 4 = 4 Marks)

5. Report Writing (7 Marks, Group Work)

This coursework evaluates your understanding the challenges of the problem and the correctness of  the proposed algorithms. It also tests your professional skills in terminology  usage, presentation  of algorithms and experimental results, as well as the logical manner of the proposal. (7 Marks)

Submission

One of the team members must submit a single zip file. The zip file is named "TeamID_Coursework.zip". It includes:  a cover letter with the group member information and the final PDF reports. The final PDF reports includes the individual reports of literature review on the sentiment analysis and the group report. A folder labeled "algorithms" contains all the model  implementations,  data  preprocessing  scripts,  and  evaluation  scripts.  A  folder  labeled "data" contains all the datasets and the experimental results in the CSV format.