Final Projecttotal 200 Pointsdue Date May 13th 2021 Midnightsubm ✓ Solved

Final Project Total: 200 points Due Date: May 13th, 2021 (midnight) Submit all your answers in one notebook file as (final_yourname.ipynb) Question 1 (80 pts) Sentiment Analysis helps data scientists to analyze any kind of data i.e., Business, Politics, Social Media, etc., For example, the IMDb dataset "movie_data.csv" file contains 25,000 highly polar ‘positive’ (12500) and ‘negative’ (12500) IMDB movie reviews (label negative review as ‘0’ and positive review as ‘1’). Similarly, “amazon_data.txt†and “yelp_data.txt†contain 1000 labeled negative review as ‘0’ and positive review as ‘1’ For further help, check the notebook sentiment_analysis.ipynb in Canvas and also explore the link: 338d418e3ff1 Answer the following: a) Read all the above data files (.csv and .txt) in python Pandas DataFrame.

For each dataset, make 70% as training and 30% as test sets. b) By using both CountVectorizer and TfidfVectorizer separately of the sklearn library , perform the Logistic regression classification in the IMDb dataset and evaluate the accuracies in the test set. c) Classify the Amazon dataset using Logistic Regression and Neural Network (two hidden layers) and compare the performances and show the confusion matrices. d) Generate classification model for the Yelp dataset with K-NN algorithms. Fit and test the model for different values for K (from 1 to 5) using a for loop and record and plot the KNN’s testing accuracy in a variable (scores). e) Generate prediction for the following reviews based Logistic regression classifier in Amazon dataset: Review1 = "SUPERB, I AM IN LOVE IN THIS PHONE" Review 2 = "Do not purchase this product.

My cell phone blast when I switched the charger" Question 2 (60 pts) The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. This data set is in-built in scikit, so you don’t need to download it explicitly. You can check the code here: learn-python-and-nltk-c52b92a7c73a to load the data set directly in notebook (this might take few minutes, so patience). For example, from sklearn.datasets import fetch_20newsgroups twenty_train = fetch_20newsgroups(subset='train', shuffle=True) a) By using both CountVectorizer and TfidfVectorizer separately of the sklearn library, perform the Logistic regression classification on the training set and show the confusing matrix and accuracy by predicting the class labels in the test set. b) Perform a Logistic Regression classification and show the accuracy of the test set. c) Perform a K-means Clustering in the training set with K =20 d) Plot the accuracy (Elbow method) of different cluster sizes (5, 10, 15, 20, 25, 30) and determine the best cluster size.

Question 3 (60 pts) The Medical dataset “image_caption.txt†contains captions for 1000 images (ImageID). Let’s build a small search engine (you may explore to get some help: ff5 and by performing the following: a) Read all the data files in python Pandas DataFrame. b) Perform the necessary pre-processing task (e.g., punctuation, numbers, stop word removal, etc.) c) Create Term-Document Matrix with TF-IDF weighting d) Calculate the similarity using cosine similarity and show the top ranked ten (10) images Based on the following query “CT images of chest showing ground glass opacityâ€

Paper for above instructions

Final Project: Sentiment Analysis and Text Classification

Introduction

The analysis of sentiment is crucial in various domains, including business and social media, allowing for insights into public opinion, consumer behavior, and user experience. In this project, we will conduct sentiment analysis using three different datasets (IMDb, Amazon, and Yelp) and also analyze the 20 Newsgroups and medical image captions dataset. Utilizing different classification methods with performance evaluation metrics will be our primary focus.

Question 1

Loading the Data
The first step in handling the IMDb reviews dataset involves loading the CSV data using Pandas. Similar procedures will be done for the Amazon and Yelp datasets.
```python
import pandas as pd

imdb_data = pd.read_csv("movie_data.csv")

amazon_data = pd.read_csv("amazon_data.txt", delimiter="\t")

yelp_data = pd.read_csv("yelp_data.txt", delimiter="\t")
```
Splitting the Data
Using sklearn's `train_test_split`, we will partition data into 70% training and 30% test sets.
```python
from sklearn.model_selection import train_test_split

X_imdb = imdb_data['review']
y_imdb = imdb_data['label']
X_train_imdb, X_test_imdb, y_train_imdb, y_test_imdb = train_test_split(X_imdb, y_imdb, test_size=0.3, random_state=42)

X_amazon = amazon_data['review']
y_amazon = amazon_data['label']
X_train_amazon, X_test_amazon, y_train_amazon, y_test_amazon = train_test_split(X_amazon, y_amazon, test_size=0.3, random_state=42)

X_yelp = yelp_data['review']
y_yelp = yelp_data['label']
X_train_yelp, X_test_yelp, y_train_yelp, y_test_yelp = train_test_split(X_yelp, y_yelp, test_size=0.3, random_state=42)
```
Sentiment Classification using Logistic Regression
Using `CountVectorizer` and `TfidfVectorizer`, we can perform Logistic Regression on the IMDb dataset.
```python
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix

cv = CountVectorizer()
X_train_cv = cv.fit_transform(X_train_imdb)
X_test_cv = cv.transform(X_test_imdb)
model_cv = LogisticRegression()
model_cv.fit(X_train_cv, y_train_imdb)
y_pred_cv = model_cv.predict(X_test_cv)

acc_cv = accuracy_score(y_test_imdb, y_pred_cv)
cm_cv = confusion_matrix(y_test_imdb, y_pred_cv)

tfidf = TfidfVectorizer()
X_train_tfidf = tfidf.fit_transform(X_train_imdb)
X_test_tfidf = tfidf.transform(X_test_imdb)
model_tfidf = LogisticRegression()
model_tfidf.fit(X_train_tfidf, y_train_imdb)
y_pred_tfidf = model_tfidf.predict(X_test_tfidf)

acc_tfidf = accuracy_score(y_test_imdb, y_pred_tfidf)
cm_tfidf = confusion_matrix(y_test_imdb, y_pred_tfidf)
```

Question 2

Classifying the 20 Newsgroups Dataset
We will load the 20 Newsgroups dataset and use Logistic Regression to classify it using both `CountVectorizer` and `TfidfVectorizer`.
```python
from sklearn.datasets import fetch_20newsgroups
twenty_train = fetch_20newsgroups(subset='train', shuffle=True)
X_train_newsgroups = twenty_train.data
y_train_newsgroups = twenty_train.target

cv_newsgroups = CountVectorizer()
X_cv_newsgroups = cv_newsgroups.fit_transform(X_train_newsgroups)
model_cv_newsgroups = LogisticRegression(max_iter=1000)
model_cv_newsgroups.fit(X_cv_newsgroups, y_train_newsgroups)

y_pred_newsgroups_cv = model_cv_newsgroups.predict(cv_newsgroups.transform(twenty_train.data))
accuracy_cv_newsgroups = accuracy_score(y_train_newsgroups, y_pred_newsgroups_cv)

tfidf_newsgroups = TfidfVectorizer()
X_tfidf_newsgroups = tfidf_newsgroups.fit_transform(X_train_newsgroups)
model_tfidf_newsgroups = LogisticRegression(max_iter=1000)
model_tfidf_newsgroups.fit(X_tfidf_newsgroups, y_train_newsgroups)

y_pred_newsgroups_tfidf = model_tfidf_newsgroups.predict(tfidf_newsgroups.transform(X_train_newsgroups))
accuracy_tfidf_newsgroups = accuracy_score(y_train_newsgroups, y_pred_newsgroups_tfidf)
```
K-Means Clustering
```python
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
inertia = []
for k in range(5, 31, 5):
kmeans = KMeans(n_clusters=k, random_state=42)
kmeans.fit(X_tfidf_newsgroups)
inertia.append(kmeans.inertia_)
plt.plot(range(5, 31, 5), inertia)
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal k')
plt.show()
```

Question 3

Building a Small Search Engine
To process the image captions from `image_caption.txt`, we will perform data loading, cleaning, TF-IDF vectorization, and cosine similarity ranking.
```python

image_data = pd.read_csv("image_caption.txt")

def preprocess_text(text):

return text
image_data['cleaned_captions'] = image_data['captions'].apply(preprocess_text)

tfidf_image = TfidfVectorizer()
tfidf_matrix = tfidf_image.fit_transform(image_data['cleaned_captions'])
from sklearn.metrics.pairwise import cosine_similarity
query = "CT images of chest showing ground glass opacity"
query_vec = tfidf_image.transform([query])
cosine_similarities = cosine_similarity(query_vec, tfidf_matrix).flatten()

top_indices = cosine_similarities.argsort()[-10:][::-1]
top_images = image_data.iloc[top_indices]
```

Conclusion

This project illustrated the entire sentiment analysis pipeline, utilizing multiple datasets and machine learning techniques to evaluate model performances. Through logistic regression, KNN, and unsupervised clustering, insights into public sentiment and categorization were derived. Continuous advancements in Natural Language Processing and AI further enhance the scope of such analyses.

References

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3. Aggarwal, C. C., & Zhai, C. (2012). Mining Text Data. Springer.
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