Sentiment Analysis

2. Using Thonny, create a python file to download the sentiment lexicon. You only need two lines of code:

import nltk

nltk.download('vader_lexicon')

3. Save the code as something like lexicon_download.py and run it. It takes only about a second.

4. Use the example code: sentiment.py

Understanding SentimentIntensityAnalyzer

The SentimentIntensityAnalyzer is part of NLTK's Vader module, which stands for Valence Aware Dictionary and sEntiment Reasoner. Here's a breakdown of how it works:

• Lexicon-Based Approach: VADER uses a lexicon (a list of lexical features, i.e., words) that are labeled according to their semantic orientation as either positive, negative, or neutral. Each word in the lexicon has a score that denotes its sentiment intensity.

• Handling Context: VADER not only examines words in isolation but also considers the context of sentences. This involves looking at:

  • Punctuation: For example, an exclamation mark can intensify the sentiment.

  • Capitalization: Using all caps can amplify a sentiment.

  • Degree Modifiers: Words like "very" or "somewhat" that can modify the intensity.

  • Conjunctions: Taking into account shifts in sentiment due to words like "but".

• Combining Scores: VADER produces four sentiment metrics:

  • Positive: Probability of the sentiment being positive.

  • Negative: Probability of the sentiment being negative.

  • Neutral: Probability of the sentiment being neutral.

  • Compound: A normalized, weighted composite score. This is often used as a singular measure of sentiment for a given text.

• Compound Score Interpretation:

  • The compound score ranges from -1 (most negative) to +1 (most positive).

  • Generally, a threshold is set (like 0.05) to classify sentiments as positive, negative, or neutral. Scores above the threshold are positive, below the threshold are negative, and those within the threshold are neutral.