Welcome to the first chapter of your journey into Natural Language Processing (NLP)!
In this chapter, we explore a specific notebook from the ML-For-Beginners project: the Romanian translation of the Sentiment Analysis lesson. While the file path includes translations/ro, the concepts of code and logic we will cover here are universal.
Imagine you are the manager of a large hotel chain. Every day, guests leave hundreds of reviews on your website.
The Use Case: You want to know: "Are our guests happy or angry?"
Reading 5,000 reviews manually would take weeks. You need a way to make a computer read them for you and instantly tell you if a review is Positive or Negative.
The Problem: Computers deal with numbers (0s and 1s), not words or feelings. They don't inherently know that "excellent" is good and "dirty" is bad.
The Solution: This notebook teaches you to use Sentiment Analysis with a tool called VADER. It translates human emotions into a numeric score between -1 (very negative) and +1 (very positive).
To understand this notebook, we need to break down a few terms:
This is the field of AI focused on enabling computers to understand human language.
In any language (English, Romanian, etc.), we use filler words like "the", "is", "at", "in". These are necessary for grammar but usually don't contain the emotion of the sentence. In NLP, we often remove them to focus on the important words (like "loved", "hated", "clean").
Analogy: Stop words are like the packing peanuts in a shipping box. They take up space, but the actual item (the meaning) is what matters.
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a specialized library in Python. It is designed specifically to detect sentiment in social media texts or reviews. It is smart enough to understand that "GOOD" (all caps) is more positive than "good" (lowercase).
Let's look at how the notebook solves the problem of analyzing hotel reviews. We will use the NLTK (Natural Language Toolkit) library.
First, we need to import the tools and download the "dictionary" that VADER uses to understand words.
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# Download the VADER lexicon (the dictionary of emotional words)
nltk.download('vader_lexicon')
# Initialize the analyzer
sia = SentimentIntensityAnalyzer()
Before analyzing, we often clean the data. If we were analyzing the sentence "The hotel was very clean", we might want to focus just on "hotel very clean".
from nltk.corpus import stopwords
# Let's say we have a list of cache words to ignore
# (In the real notebook, we load these from NLTK)
cache_english_stopwords = stopwords.words('english')
def remove_stopwords(text):
words = text.split()
# Keep word only if it is NOT in the stopword list
clean_words = [w for w in words if w not in cache_english_stopwords]
return " ".join(clean_words)
Explanation: This code takes a sentence, breaks it into individual words, throws away the "packing peanuts" (stop words), and glues the important words back together.
This is the magic moment. We ask VADER to read a sentence and give us a score.
# A sample review
review = "I absolutely loved the breakfast!"
# Get the score
score = sia.polarity_scores(review)
print(score)
What happens here?
The output score will look something like this dictionary:
{'neg': 0.0, 'neu': 0.4, 'pos': 0.6, 'compound': 0.65}
How does the computer know "loved" is positive? It doesn't actually "feel" anything. It uses a Lexicon (a scored dictionary).
Here is a simplified view of the process inside the notebook:
In the actual notebook, we apply this logic to a whole column of data (thousands of reviews) using Python's pandas library.
# Assuming 'df' is our table of hotel reviews
# We create a new column 'sentiment' for the results
def get_sentiment(review_text):
# Get the dictionary of scores
scores = sia.polarity_scores(review_text)
# Return just the compound (overall) score
return scores['compound']
# Apply this function to every review
# df['sentiment'] = df['Review_Text'].apply(get_sentiment)
Explanation: This creates a loop that goes through every single review in your spreadsheet, calculates the score, and saves it in a new column. You can then compare this sentiment score with the user's Star Rating to see if they match!
In this chapter, we explored translations/ro/6-NLP/5-Hotel-Reviews-2/notebook.ipynb. We learned that computers can "read" emotions by treating words as data points with mathematical values.
Recap:
You have now taken your first step into Natural Language Processing! In future chapters, we will look at how to train your own models instead of using pre-made dictionaries like VADER.
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