Using Natural Language Processing to prevent suicide

The Basics

Natural Language Processing

Sentiment Analysis to prevent Suicide

Step 1

# TFIDF Vector
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
X = df['tweet']
tfidf = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))
X = tfidf.fit_transform(X)
y = df['intention']

Step 2

import warnings
warnings.simplefilter("ignore", DeprecationWarning)# Load the LDA model from sk-learn
from sklearn.decomposition import LatentDirichletAllocation as LDA

# Helper function
def print_topics(model, count_vectorizer, n_top_words):
words = count_vectorizer.get_feature_names()
for topic_idx, topic in enumerate(model.components_):
print("\\nTopic #%d:" % topic_idx)
print(" ".join([words[i]
for i in topic.argsort()[:-n_top_words - 1:-1]]))

# Tweak the two parameters below
number_topics = 5
number_words = 10# Create and fit the LDA model
lda = LDA(n_components=number_topics, n_jobs=-1)
lda.fit(count_data)# Print the topics found by the LDA model
print("Topics found via LDA:")
print_topics(lda, count_vectorizer, number_words)
Topic 1: Possibly Suicide
Words: "kill" , "die" , "death" , *"worthless" , "murder" , "self-murder" , "depressed", "Lonely"
Topic 2: Normal
Words:"american","alright" , "covid-19" , "good" , "lover"

Let’s test the model!

X = 'I just want my life to end already'
vec = tfidf.transform([X])
clf.predict(vec)
array([1])
X = 'congratulations, you have done it'
vec = tfidf.transform([X])
array([0])

Training and testing our model

The Future

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15 yo futurist, change maker and innovator at The Knowledge Society.

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Alisha Arora

Alisha Arora

15 yo futurist, change maker and innovator at The Knowledge Society.

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