How to do sentiment analysis with topic modeling or NER [ Python]?

Question

I have the following code for sentiment analysis. I was wondering how can I include topic modeling or NER within it? (the dataset is of costumers' reviews of 3 websites, a csv file of 2 columns, one the reviews and one the rating of 0 as negative and 1 for positive)

 from nltk.corpus import stopwords
    from nltk.stem.porter import PorterStemmer
    
    dataset = pd.read_csv('full_db.csv') 
    X = dataset.iloc[:,0].values
    y = dataset.iloc[:, 1].values
    
    corpus = []
    
    for i in range(0, len(X)):
        review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])  #replace punctuations with space
        review = review.lower()  #transfering all the letters to lower-case
        review = review.split()  #spliting the review into words
        #apply stemming
        ps = PorterStemmer()
        all_stopwords = stopwords.words('english')
        no_stopwords = ["not","don't",'aren','don','ain',"aren't", 'couldn', "couldn't", "wasn't"]
        for Nostopword in no_stopwords:
            all_stopwords.remove(Nostopword)
        review = [ps.stem(word) for word in review if not word in set(all_stopwords)] 
        review = ' '.join(review)
        corpus.append(review)

    #Splitting the dataset into Training set and Test set
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=42)
    
    #logistic regression
    # Initialize a logistic regression model 
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import confusion_matrix, accuracy_score,classification_report
    logistic = LogisticRegression(random_state=42, solver='lbfgs',
                                multi_class='multinomial')
    # Train the model
    logistic = logistic.fit(X_train, y_train)
    y_pred = logistic.predict(X_test)

Answer 1

I don't know how to do NER, Topic Modeling, and Sentiment Analysis in the same model, but I can show you how to do NER and Topic Modeling with two separate models here. I have been working in NLP for a while and wrote both of the articles I pull code from in this answer.

You can use NLTK packages to do NER on your corpus without needing to train an additional model. (Code taken from this article on Named Entity Recognition )

Run in Terminal to get the packages:

>>> import nltk
>>> nltk.download(“punkt”)
>>> nltk.download(“averaged_perceptron_tagger”)
>>> nltk.download(“maxent_ne_chunker”)
>>> nltk.download(“words”)

Python:

import nltk
 
text = "Molly Moon is a cow. She is part of the United Nations' Climate Action Committee."
 
tokenized = nltk.word_tokenize(text)
pos_tagged = nltk.pos_tag(tokenized)
chunks = nltk.ne_chunk(pos_tagged)
for chunk in chunks:
    if hasattr(chunk, 'label'):
        print(chunk)

For topic modeling, you can use BERTopic. (Code pulled from this article on Topic Modeling ) The docs variable should be a list of document text.

# creating and fitting model
from bertopic import BERTopic
model = BERTopic()
topics, probs = model.fit_transform(docs)
# plotting model
import numpy as np
import pandas as pd
from umap import UMAP
 
import matplotlib
import matplotlib.pyplot as plt
 
%matplotlib inline
 
# Prepare data for plotting
embeddings = model._extract_embeddings(docs, method="document")
umap_model = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit(embeddings)
df = pd.DataFrame(umap_model.embedding_, columns=["x", "y"])
df["topic"] = topics
 
# Plot parameters
top_n = 10
fontsize = 12
 
# Slice data
to_plot = df.copy()
to_plot[df.topic >= top_n] = -1
outliers = to_plot.loc[to_plot.topic == -1]
non_outliers = to_plot.loc[to_plot.topic != -1]
 
# Visualize topics
cmap = matplotlib.colors.ListedColormap(['#FF5722', # Red
                                        '#03A9F4', # Blue
                                        '#4CAF50', # Green
                                        '#80CBC4', # FFEB3B
                                        '#673AB7', # Purple
                                        '#795548', # Brown
                                        '#E91E63', # Pink
                                        '#212121', # Black
                                        '#00BCD4', # Light Blue
                                        '#CDDC39', # Yellow/Red
                                        '#AED581', # Light Green
                                        '#FFE082', # Light Orange
                                        '#BCAAA4', # Light Brown
                                        '#B39DDB', # Light Purple
                                        '#F48FB1', # Light Pink
                                        ])
 
# Visualize outliers + inliers
fig, ax = plt.subplots(figsize=(15, 15))
scatter_outliers = ax.scatter(outliers['x'], outliers['y'], c="#E0E0E0", s=1, alpha=.3)
scatter = ax.scatter(non_outliers['x'], non_outliers['y'], c=non_outliers['topic'], s=1, alpha=.3, cmap=cmap)
 
# Add topic names to clusters
centroids = to_plot.groupby("topic").mean().reset_index().iloc[1:]
for row in centroids.iterrows():
   topic = int(row[1].topic)
   text = f"{topic}: " + "_".join([x[0] for x in model.get_topic(topic)[:3]])
   ax.text(row[1].x, row[1].y*1.01, text, fontsize=fontsize, horizontalalignment='center')
 
ax.text(0.99, 0.01, f"BERTopic - Top {top_n} topics", transform=ax.transAxes, horizontalalignment="right", color="black")
plt.xticks([], [])
plt.yticks([], [])
plt.savefig("BERTopic_Example_Cluster_Plot.png")
plt.show()